Video prediction using one or more neural networks

ABSTRACT

Apparatuses, systems, and techniques to enhance video are disclosed. In at least one embodiment, one or more neural networks are used to create, from a first video, a second video having one or more additional video frames.

FIELD

At least one embodiment pertains to processing resources used to perform and facilitate artificial intelligence. For example, at least one embodiment pertains to processors or computing systems used to train neural networks according to various novel techniques described herein.

BACKGROUND

As video content is being consumed in an ever-increasing variety of ways, on varying devices and from varying sources, there can be instances of video content that may be less than optimal for a particular use or application. For example, a video may be at a lower frame rate or resolution that would be desired for a particular viewing device. There might also be one or more frame drops, or there may be an error in one or more frames of data. It might also happen that there is less video content in a file than is desired.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will be described with reference to the drawings, in which:

FIGS. 1A, 1B, 1C, 1D, 1E, and 1F illustrate image data that can be analyzed, according to at least one embodiment;

FIG. 2 illustrates components of a video frame analysis system, according to at least one embodiment;

FIGS. 3A and 3B illustrate video frame predictions, according to at least one embodiment;

FIG. 4 illustrates a video frame inferencing system, according to at least one embodiment'

FIGS. 5A and 5B illustrate portions of a process for inferring one or more video frames, according to at least one embodiment;

FIG. 6 illustrates a system for training and inferencing using one or more neural networks. according to at least one embodiment;

FIG. 7 illustrates a system for training one or more neural networks. according to at least one embodiment;

FIG. 8 illustrates a structure of a neural network, according to at least one embodiment;

FIG. 9A illustrates inference and/or training logic, according to at least one embodiment;

FIG. 9B illustrates inference and/or training logic, according to at least one embodiment;

FIG. 10 illustrates an example data center system, according to at least one embodiment;

FIG. 11 illustrates a computer system, according to at least one embodiment;

FIG. 12 illustrates a computer system, according to at least one embodiment;

FIG. 13 illustrates a computer system, according to at least one embodiment;

FIG. 14 illustrates a computer system, according at least one embodiment;

FIG. 15A illustrates a computer system, according to at least one embodiment;

FIG. 15B illustrates a computer system, according to at least one embodiment;

FIG. 15C illustrates a computer system, according to at least one embodiment;

FIG. 15D illustrates a computer system, according to at least one embodiment;

FIGS. 15E and 15F illustrate a shared programming model, according to at least one embodiment;

FIG. 16 illustrates exemplary integrated circuits and associated graphics processors, according to at least one embodiment;

FIGS. 17A-17B illustrate exemplary integrated circuits and associated graphics processors, according to at least one embodiment;

FIGS. 18A-18B illustrate additional exemplary graphics processor logic, according to at least one embodiment;

FIG. 19 illustrates a computer system, according to at least one embodiment;

FIG. 20A illustrates a parallel processor, according to at least one embodiment;

FIG. 20B illustrates a partition unit, according to at least one embodiment;

FIG. 20C illustrates a processing cluster, according to at least one embodiment;

FIG. 20D illustrates a graphics multiprocessor, according to at least one embodiment;

FIG. 21 illustrates a multi-graphics processing unit (GPU) system, according to at least one embodiment;

FIG. 22 illustrates a graphics processor, according to at least one embodiment;

FIG. 23 illustrates a processor's micro-architecture, according to at least one embodiment;

FIG. 24 illustrates a deep learning application processor, according to at least one embodiment;

FIG. 25 illustrates an example neuromorphic processor, according to at least one embodiment;

FIGS. 26 and 27 illustrate at least portions of a graphics processor, according to at least one embodiment;

FIG. 28 illustrates at least portions of a graphics processor core, according to at least one embodiment;

FIGS. 29A-29B illustrate at least portions of a graphics processor core, according to at least one embodiment;

FIG. 30 illustrates a parallel processing unit (“PPU”), according to at least one embodiment;

FIG. 31 illustrates a general processing cluster (“GPC”), according to at least one embodiment;

FIG. 32 illustrates a memory partition unit of a parallel processing unit (“PPU”), according to at least one embodiment; and

FIG. 33 illustrates a streaming multi-processor, according to at least one embodiment.

DETAILED DESCRIPTION

In at least one embodiment, a sequence 100 of video frames can be received on a video stream as illustrated in FIG. 1A. In at least one embodiment, successive video frames can include variations from earlier video frames. In at least one embodiment, a frame rate at which video is captured or created can determine not only how often an image in video refreshes, but also how much variation occurs between frames. In at least one embodiment, it may be desirable to improve a frame rate, or number of frames per second, in this video stream, or in a related video file or clip. In at least one embodiment, increasing a frame rate can improve an experience of a viewer of this video stream, or can improve a perceived quality of this video stream. In at least one embodiment, one or more of these video frames may be dropped from this video stream due to an error, such as illustrated in a video stream sequence 120 of FIG. 1B. In at least one embodiment, both situations of FIG. 1A and 1B can benefit from an addition of video frames to improve a frame rate of this video or replace one or more dropped frames. In at least one embodiment, such an approach can result in a video stream sequence 140 such as that illustrated in FIG. 1C, where any dropped frames are replaced and an overall frame rate is increased. In at least one embodiment, it may be desirable to make either, both, or none of these corrections, but to increase a resolution of this video stream.

In at least one embodiment, video frame prediction can be performed using a current video frame 160, such as that illustrated in FIG. 1D. In at least one embodiment, a sequence of such frames can be analyzed to model long-term video sequences. In at least one embodiment, a machine learning-based approach can be used that takes advantage of a latent representation to attempt to map video frames into a low dimensional, compact space. In at least one embodiment, one or more neural networks can be trained to model how that latent representation changes over time to find this mapping back to an original video frame. In at least one embodiment, one or more constraints are placed on this latent space in order to enable this latent space to be able to be interpreted. In at least one embodiment, a constraint involves modeling a set of coordinates that move through time, where those moving coordinates can be used to adjust an appearance of one or more objects in a corresponding video file.

In at least one embodiment, one or more neural networks can be trained to predict or infer video frames for one or more future points in time, at least with respect to playback time of a video, given one or more video frames from past points in time. In at least one embodiment, features such as unsupervised landmarks can be used to represent various features of an object for purposes of modeling movement or other changes over time, and between adjacent frames in video data. In at least one embodiment, features such as these landmarks can be used to model motion of an object, such as a person, through a sequence of video frames. In at least one embodiment, a pose detector can be used to determine feature points, as may correspond to limbs and joints of a person represented in a video, and infer motion of person by moving those feature points between frames. In at least one embodiment, a trained model can map feature points, with their inferred motion, back to image data for this video frame to infer a subsequent video frame including a representation of that person at a future point in time based on past observed motion or behavior. In at least one embodiment, features such as unsupervised landmarks can be used to approximate key points that represent an object but without a need to manually annotate a significant amount of training data. In at least one embodiment, a pose detector can be used to analyze a video frame, such as frame 160 of FIG. 1D that includes a representation of a person 162, and generate pose information 182 as illustrated in frame 180 of FIG. 1E. In at least one embodiment, frame 180 includes regions that correspond to moving parts of this person 182, with intensity or other values that enable determination of feature points, or unsupervised landmarks, in this image data. In at least one embodiment, an intensity value must at least satisfy a minimum feature threshold value before being considered or selected as an image feature. In at least one embodiment, a centroid calculation or absolute maximum approach can be used to determine a coordinate for a specific feature point, as may be selected from a set of coordinate selection algorithms. In at least one embodiment, these feature points can be considered to be unsupervised landmarks since there are no annotations for any of these feature points, such that a pose detector may determine a coordinate for an elbow but will not have knowledge that this point corresponds to an elbow. In at least one embodiment, some annotation may be performed using image reconstruction or another such process, where knowledge of a type of point may be considered when modeling motion of an object over time. In at least one embodiment, such a process can be used for long-term video prediction, such as for a sequence 190 of frames in a video stream in FIG. 1F, where a number of frames 192 are predicted using information from one or more prior frames.

In at least one embodiment, a system 200 for analyzing input video frames can include components as illustrated in FIG. 2. In at least one embodiment, system 200 includes three primary components, including an image encoder 222, an image decoder 216, and a temporal dynamics encoder 214. In at least one embodiment, a video frame 202 is received as input to image encoder 222. In at least one embodiment, image encoder 222 is configured to project video frame 202 into a factorized pose-appearance space. In at least one embodiment, image encoder 222 includes a pose encoder 204 for determining pose information for input frame 202, and an appearance encoder 206 for determining appearance information for input frame 202. In at least one embodiment, pose data 208 can be determined by pose encoder 204 that can be used with a pose analyzer 212 to generate pose information for a time point t of input video frame 202 that can be passed to temporal encoder 214. In at least one embodiment, appearance information determined from appearance encoder 206 can be passed directly to image decoder 216. In at least one embodiment, image encoder 216 can take this appearance information and use it with predicted pose data (as data in pose-appearance space) to (re)construct a video frame 220 that should correspond to input frame 202, in order to learn landmarks for modeling motion.

In at least one embodiment, pose data from pose encoder 204 is passed to temporal encoder 214 to attempt to determine how that pose representation would change, or differ, for one or more future points in time (e.g., t+2). In at least one embodiment, time refers to playback time of a video, as a stored video time may be viewed at various points in time. In at least one embodiment, for video prediction and interpolation it can be assumed, at least for a determined time period, that appearance information in this video will be constant and changes can be captured through pose representation. In at least one embodiment, given factorization of a frame into pose and appearance, temporal dynamics encoder 214 can predict how pose representation changes over time. In at least one embodiment, at each time step image decoder 216 can then construct new frame 220 using constant appearance information and updated pose representation.

In at least one embodiment, such an approach can be used to predict video frames based upon one or more input frames. In at least one embodiment, video streams 300, 350 predicted using such an approach but without ground truth data can be produced as illustrated in FIGS. 3A and 3 B. In at least one embodiment, as compared to corresponding ground truth data, inferred frames correspond well with ground truth data for shorter periods of time, but as an amount of time increases without another reference frame noticeable differences can appear and become more significant. In at least one embodiment, even with fine-tuning, video streams may only be upscaled in resolution or frame rate by a certain amount without suffering from potentially noticeable artifacts.

In at least one embodiment, such an approach can be used for prediction and interpolation for long-range video data. In at least one embodiment, video prediction and interpolation are modeled as an unsupervised latent structure inference followed by a temporal prediction in latent space. In at least one embodiment, latent representations can capture foreground semantics without explicit supervision, such as may utilize keypoints or annotated pose data. In at least one embodiment, each landmark can be mapped to a coordinate indicating where a semantic part is positioned, allowing for reliable interpolation within a coordinate domain to achieve predictable motion interpolation. In at least one embodiment, an image decoder can map these landmarks back to an image domain, achieving high-quality long-range video interpolation and extrapolation through operating on a landmark representation space. In at least one embodiment, a self-supervised pipeline is used to model long-term video dynamics in a latent, explicit pose representation. In at least one embodiment, rough object-level bounding boxes are used for training purposes.

In at least one embodiment, a latent representation of K landmark “keypoints” can be learned, each describing a spatially local appearance and 2D coordinates of a single rigid component. In at least one embodiment, this representation is interpretable, and follows naturally when modeling dynamics of linked or highly correlated rigid objects in a scene. In at least one embodiment, unsupervised landmark identification processes can be utilized which can learn localized landmarks that activate consistently on same semantic locations, independent of a class of typical transformations. In at least one embodiment, this can be extended to use unsupervised landmarks for long-range video frame prediction and interpolation. In at least one embodiment, a constrained and parameterized representation can be both sufficiently expressive and stable to achieve long-range quality estimates for video prediction, while also providing intuitive results during interpolation tasks. In at least one embodiment, pose representations can be manipulated based at least in part upon two-dimensional (2D) Gaussian heat maps in both predictive and interpolative settings.

In at least one embodiment, pose and appearance factorization is learned using unsupervised landmarks by separating an input image into an appearance-invariant pose representation and a pose-invariant appearance representation, then attempting to reconstruct an original input image in order to learn landmarks. In at least one embodiment, a network can be built using three models: an image-to-pose encoder, an image to appearance encoder, and an image decoder that attempts to reconstruct an input image from factorized pose and appearance representations.

In at least one embodiment, pose encoder Φ^(pose)=Enc^(pose) (x) can take an input image x and output a set of consistent part activation maps. In at least one embodiment, these part activation maps are be invariant to changes in local appearance, and are consistent across deformations. In at least one embodiment, a heat map that activates on a person's right hand should be invariant across varying skin tones and lighting conditions, as well track this right hand's location across varying deformations and translations. In at least one embodiment, appearance encoder Φ^(app)=Enc^(app) (x; Enc^(pose) (x)) extracts local appearance information, conditioned on activation maps of a pose-encoder. In at least one embodiment, given an input image x, this pose encoder will first provide K×H×W part activation maps Φ^(oise). In at least one embodiment, to extract local appearance vectors, an appearance encoder projects this image to a C×H×W appearance feature map M^(app). In at least one embodiment, an appearance vector for a kth pose activation map can be computed as given by:

${\Phi_{k,c}^{app} = {{\sum\limits_{i}^{H}{\sum\limits_{j}^{W}{\Phi_{k,i,j}^{pose}M_{c,i,j}^{app}\mspace{14mu} {for}\mspace{14mu} c}}} = {1\mspace{14mu} \ldots \mspace{14mu} C}}},$

giving K C-dimensional appearance vectors. In at least one embodiment, each activation map in Φ^(pose) is softmax-normalized.

In at least one embodiment, an image decoder attempts to reconstruct an original input image by combining pose information from K activation maps with pooled appearance vectors for each of K parts. In at least one embodiment, instead of conditioning on raw softmax-normalized activation pose maps, a 2D Gaussian can be fit to each activation of these K activation maps by estimating their respective means and covariance matrices. In at least one embodiment, each part is represented {tilde over (Φ)}_(k) ^(pose)=(μ_(k), Σ_(k)), where ∈

² and ∈

^(2×2). In at least one embodiment, a 2D Gaussian approximation enables manipulation of a pose representation as it forces each part activation map into a unimodal representation with a simple parameterization. In at least one embodiment, a pose encoder, shape encoder, and image decoder are jointly trained in a fully self-supervised fashion, using a final image reconstruction task as a sole source of supervision.

In at least one embodiment, color jittering and thin-plate-spline (TPS) warping are incorporated into a training pipeline to enforce appearance invariance and localization properties, respectively, of a pose encoder. In at least one embodiment, let T_(cj)(x) and T_(tps)(x) represent color jittering and thin-plate spline warping operations on input image x respectively. In at least one embodiment, a training procedure can be expressed as follows:

Φ^(pose) =Enc ^(pose)(T _(cj)(x))

Φ^(app) =Enc ^(app)(T _(tps)(x); Enc ^(pose)(T _(tps)(x)))

{tilde over (x)}=Dec ({tilde over (Φ)}^(pose), Φ^(app)

where reconstruction loss between original input x and reconstruction {tilde over (x)} can be minimized.

In at least one embodiment, neither a shape encoder nor an appearance encoder are ever given access to un-perturbed target x. In at least one embodiment, pose information feeding into image decoder Dec(. , .) is based on a color-jittered input image, where only local appearance information is perturbed. In at least one embodiment, appearance information is computed from a thin-plate-spline warped version of an input image, where pose information is perturbed. In at least one embodiment, as a shape encoder is also executed on this TPS-warped input image, it can localize corresponding locations in new warped coordinates in order for correct appearance information to be captured. In at least one embodiment, existing motion in input video data is also used as a source of pose perturbation. In at least one embodiment, in addition to a T_(cj)(x)-T_(tps)( x) pairing, an additional pairing can be included where T_(tps) is replaced by sampling a random video frame between 3 and 60 frames away from current frame x.

In at least one embodiment, 2D Gaussian parameters from {tilde over (Φ)}^(pose) are projected to a heat map of target output width and height to use as semantic maps. In at least one embodiment, a formula to be used is given by:

${s\left( {k,l} \right)} = \frac{1}{1 + {\left( {l - \mu_{k}} \right)^{T}{\sum\limits_{k}^{- 1}\left( {l - \mu_{k}} \right)}}}$

where s(k, l) is a heatmap value for part map k at coordinate location l.

In at least one embodiment, for losses a VGG perceptual-loss can be utilized with pre-trained ImageNet weights, combined with an adversarial loss to improve realism and a standard pixel-level mean square error loss. In at least one embodiment, reconstruction loss can be expressed as follows:

_(recon)(x,{tilde over (x)})=λ_(vgg)

_(vgg)(x, {tilde over (x)})+λ_(MSE) MSE(x, {tilde over (x)})+

_(adv) ({tilde over (x)})

In at least one embodiment, since an unsupervised landmarks-based pose representation can be explicitly interpreted as localized part labels, it can be natural to manipulate and interpolate within this space. In at least one embodiment, however, care can be taken to ensure that a covariance matrix for each landmark remains positive definite throughout any manipulation. In at least one embodiment, this can be done in many parameterizations, such as directly in covariance parameters, indirectly in parameters of a Cholesky decomposition or an angle and magnitude of principal components, or even along an optimal transport as a Wasserstein Barycenter. In at least one embodiment, owing to a wide variety of possible projected deformations, none of these methods dominate in all conditions.

In at least one embodiment, parameters of a Cholesky decomposition are utilized because they can be straightforward to compute and guarantee covariance validity in both interpolation and extrapolation paradigms. In at least one embodiment, Cholesky decomposition of each covariance matrix is a unique matrix

_(k) with positive diagonal such that Σ_(k)=L_(k)L_(k) ^(T) where L_(k) is a lower-triangular. In at least one embodiment, for 2-dimensional keypoints as discussed herein, this can result in three scalars (e.g., two positive, one unconstrained) which uniquely parameterize a covariance of each keypoint. In at least one embodiment, when coupled with two dimensional mean μ_(k), these five parameters form a state vector for keypoint k at timestep t. In at least one embodiment, interpolation is done linearly as may be given by:

$\begin{bmatrix} {\hat{\mu}}_{k} \\ {\hat{L}}_{k} \end{bmatrix}_{t = \alpha} = {{\left( {1 - \alpha} \right)\begin{bmatrix} \mu_{k} \\ L_{k} \end{bmatrix}}_{t = 0} + {\alpha \begin{bmatrix} \mu_{k} \\ L_{k} \end{bmatrix}}_{t = 1}}$

In at least one embodiment, a long short-term memory (LSTM) can be used to model non-linear temporal dynamics of pose representation. In at least one embodiment, as for interpolation, care must be taken to maintain positive definite covariance matrices during prediction. In at least one embodiment, parameters of a Cholesky decomposition of covariance matrices are used as prediction targets. In at least one embodiment, an LSTM may produce an estimate for L which is not a valid Cholesky factor, but even this case will still produce a valid covariance matrix when LL^(T) is computed.

In at least one embodiment, a pair of LSTMs can be used that can predict perturbations to Gaussian means and Cholesky parameters of covariances. In at least one embodiment, in addition to Cholesky parameterization, predicting a residual during extrapolation, rather than state directly, helped ensure robust long term predictions. In at least one embodiment, this state residual, or error state, can be used to improve estimation performance in a variety of tasks with non-linear dynamics. In at least one embodiment, in addition to maintaining an unconstrained gradient path stretching far back into time, an error-state can also induce a model to learn local deformations of state, which can be easier to learn as a result of being small, and nearly linear. In at least one embodiment, this can help improve both training performance and generalization. In at least one embodiment, at each time-step an LSTM takes a concatenation of previous state and state residual to predict a next state residual.

In at least one embodiment, a self-supervised paradigm for long range video prediction and interpolation is capable of preserving image structure over long time frames. In at least one embodiment, this is achieved by factorizing image data into pose information and temporally invariant appearance information. In at least one embodiment, as pose information will go through numerous perturbations throughout long-range video prediction, collections of unsupervised 2D Gaussian landmarks can be used as pose state. In at least one embodiment, each 2D Gaussian can be safely perturbed in Gaussian parameter space (μ, Σ) before projecting to 2D heatmaps to be fed into an image decoder, thereby guaranteeing that an image decoder will see a collection of unimodal Gaussian heat maps. In at least one embodiment, a pose representation is a collection of 2D anisotropic Gaussian heat maps. In at least one embodiment, a strict Gaussian representation enables manipulation in Gaussian parameter space while still guaranteeing a Gaussian at each time step. In at least one embodiment, each Gaussian can be interpreted as a localization of an object “part” such as a wrist or, leg, where Covariance roughly captures spatial support of said part. In at least one embodiment, Gaussian landmarks are learned without need for any manual part annotation. In at least one embodiment, parts that are learned are determined completely during training.

In at least one embodiment, Gaussians can be manipulated temporally in Gaussian parameter space (e.g., values of mean, covariance), thereby guaranteeing that a pose representation is a strictly a collection of Gaussian heat maps at every time step. In at least one embodiment, a determined translation can then be applied to these Gaussians. In at least one embodiment, covariance may be trickier as it may need to be invertible and positive definite after any predicted perturbation. In at least one embodiment, a Cholesky decomposition into a lower triangular matrix L can be performed. In at least one embodiment, matrix L can be perturbed and new covariance reconstructed with LL^(T), thereby guaranteeing a positive definite covariance matrix at every step.

In at least one embodiment, unsupervised landmarks or other features obtained for a video frame can then have a smoothing process applied for free space. In at least one embodiment, each unsupervised landmark is represented by a Gaussian heat map that can be parameterized as a mean and a covariance, with mean being a center coordinate for a respective feature. In at least one embodiment, a temporal dynamics model (e.g., an LSTM used for prediction) can be trained to determine, given a sequence of these coordinates, how these coordinates shift over time. In at least one embodiment, a new layout of these color distributions can be predicted over time. In at least one embodiment, this layout can be fed to another network, whether part of image decoder 216 or as part of a separate video generator 402 as illustrated in FIG. 4, that maps it back to image space to create an inferred video. In at least one embodiment, linear interpolation can be used to capture determined motion in a sequence of inferred video frames. In at least one embodiment, one or more physical simulators can be used to infer how these Gaussian coordinates might move over time. In at least one embodiment, an inferred video includes one or more additional video frames, whether to add new content, fill in missing content, or improve a quality of existing content, such as to improve a frame rate or resolution of an existing video file or stream.

In at least one embodiment, a process 500 illustrated in FIG. 5A can be used to train a model to infer video frames for purposes such as enhancing video data. In at least one embodiment, one or more video frames are received 502 or otherwise obtained, such as may be part of a video file or stream. In at least one embodiment, frames from this video are provided as input training data to a training system for training one or more neural networks. In at least one embodiment, a current video frame is analyzed 504 to determine pose and appearance representations for a current video frame. In at least one embodiment, an unsupervised landmarks approach can be used where features in this pose representation are represented as Gaussian heat maps that can each be parameterized as a mean and a covariance. In at least one embodiment, these heat maps can be used to predict 506 one or more pose representations, as may correspond to one or more future points or periods in time. In at least one embodiment, pose representation information can be used 508 with appearance representation information to reconstruct a current video frame being analyzed. In at least one embodiment, input and reconstructed versions of a current video frame are compared 510 to determine a current loss value using an appropriate loss function. In at least one embodiment, one or more network parameters can be adjusted 512 in order to attempt to minimize this loss. A determination can be made 514 as to whether more frames should be analyzed. In at least one embodiment, determining factors include whether there are additional frames in this video file or stream, as well as whether a model convergence or training completion criterion has been satisfied. In at least one embodiment, if more frames are to be analyzed then this process can continue with another input video frame. In at least one embodiment, if no more frames are to be analyzed then an enhanced video stream or file can be provided 516 that includes at least some inferred frames, where this video may have a higher resolution, higher frame rate, sequence of new frames, or lower number of dropped or missing frames that an originally input video or set of video frames.

In at least one embodiment, a process 550 illustrated in FIG. 5B can be used, at inference time, to infer video frames for purposes such as enhancing video data. In at least one embodiment, one or more video frames of a video file or stream can be received 552 to a trained model. In at least one embodiment, input frames from this video can be used to infer 554, using this trained model, additional video frames. In at least one embodiment, these inferred video frames can be utilized 566 to generate an enhanced video stream or file.

Neural Network Training And Development

An increasing variety of industries and applications are taking advantage of machine learning. In at least one embodiment, deep neural networks (DNNs) developed on processors have been used for diverse use cases, from self-driving cars to faster drug development, from automatic, image analysis for security systems to smart real-time language translation in video chat applications. In at least one embodiment, deep learning is a technique that models a neural learning process of a human brain, continually learning, continually getting smarter, and delivering more accurate results more quickly over time. A child is initially taught by an adult to correctly identify and classify various shapes, eventually being able to identify shapes without any coaching. Similarly, in at least one embodiment a deep learning or neural learning system designed to accomplish a similar task would need to be trained for it to get smarter and more efficient at identifying basic objects, occluded objects, etc., While also assigning context to those objects.

In at least one embodiment, neurons in a human brain look at various inputs that are received, importance levels are assigned to each of these inputs, and output is passed on to other neurons to act upon. An artificial neuron or perceptron is a most basic model of a neural network. In at least one embodiment, a perceptron may receive one or more inputs that represent various features of an object that a perceptron is being trained to recognize and classify, and each of these features is assigned a certain weight based on importance of that feature in defining a shape of an object.

A deep neural network (DNN) model includes multiple layers of many connected perceptrons (e.g., nodes) that can be trained with enormous amounts of input data to quickly solve complex problems with high accuracy. In one example, a first layer of a. DNN model breaks down an input image of an automobile into various sections and looks for basic patterns such as lines and angles. Second layer assembles lines to look for higher-level patterns such as wheels, windshields, and mirrors. A next layer identifies a type of vehicle, and a final few layers generate a label for an input image, identifying a model of a specific automobile brand. Once a DNN is trained. this DNN can be deployed and used to identify and classify objects or patterns in a process known as inference. Examples of inference (a process through which a DNN extracts useful information from a given input) include identifying handwritten numbers on checks deposited into ATM machines, identifying images of friends in photos, delivering movie recommendations, identifying and classifying different types of automobiles, pedestrians, and road hazards in driverless cars, or translating human speech in near real-time.

During training, data flows through a DNN in a forward propagation phase until a prediction is produced that indicates a label corresponding to input. if a neural network does not correctly label input, then errors between a correct label and a predicted label are analyzed, and weights are adjusted for each feature during a backward propagation phase until a DNN correctly labels input and other inputs in a training dataset. Training complex neural networks requires massive amounts of parallel computing performance, including floating-point multiplications and additions that are supported. Inferencing is less compute-intensive than training, being a latency-sensitive process where a trained neural network is applied to new inputs it has not seen before to classify images, translate speech, and infer new information.

Neural networks rely heavily on matrix math operations, and complex multi-layered networks require tremendous amounts of floating-point performance and bandwidth for both efficiency and speed. With thousands of processing cores, optimized for matrix math operations, and delivering tens to hundreds of TFLOPS of performance, a computing platform can deliver performance required for deep neural network-based artificial intelligence and machine learning applications.

FIG. 6 illustrates components of a system 600 that can be used to train and utilize machine learning, in at least one embodiment. As will be discussed, various components can be provided by various combinations of computing devices and resources, or a single computing system, which may be under control of a single entity or multiple entities. Further, aspects may be triggered, initiated, or requested by different entities. In at least one embodiment training of a neural network might be instructed by a provider associated with provider environment 606, while in at least one embodiment training might be requested by a customer or other user having access to a provider environment through a client device 602 or other such resource. In at least one embodiment, training data (or data to be analyzed by a trained neural network) can be provided by a provider, a user, or a third party content provider 624. In at least one embodiment, client device 602 may be a vehicle or object that is to be navigated on behalf of a user, for example, which can submit requests and/or receive instructions that assist in navigation of a device.

In at least one embodiment, requests are able to be submitted across at least one network 604 to be received to a provider environment 606. In at least one embodiment, a client device may be any appropriate electronic and/or computing devices enabling a user to generate and send such requests, as may include desktop computers, notebook computers, computer servers, smartphones, tablet computers, gaming consoles (portable or otherwise), computer processors, computing logic, and set-top boxes. Network(s) 604 can include any appropriate network for transmitting a request or other such data, as may include Internet, an intranet, an Ethernet, a cellular network, a local area network (LAN), a network of direct wireless connections among peers, and so on.

In at least one embodiment, requests can be received to an interface layer 608, which can forward data to a training and inference manager 610 in this example. This manager can be a system or service including hardware and software for managing requests and service corresponding data or content. In at least one embodiment, this manager can receive a request to train a neural network, and can provide data for a request to a training manger 612. In at least one embodiment, training manager 612 can select an appropriate model or network to be used, if not specified by a request, and can train a model using relevant training data. In at least one embodiment training data can be a batch of data stored to a training data repository 614, received from client device 602 or obtained from a third party provider 624. In at least one embodiment, training manager 612 can be responsible for training data, such as by using a LARC-based approach as discussed herein. A network can be any appropriate network, such as a recurrent neural network (RNN) or convolutional neural network (CNN). Once a network is trained and successfully evaluated, a trained network can be stored to a model repository 616, for example, that may store different models or networks for users, applications, or services, etc. In at least one embodiment there may be multiple models for a single application or entity, as may be utilized based on a number of different factors.

In at least one embodiment, at a subsequent point in time, a request may be received from client device 602 (or another such device) for content (e.g., path determinations) or data that is at least partially determined or impacted by a trained neural network. This request can include, for example, input data to be processed using a neural network to obtain one or more inferences or other output values, classifications, or predictions. In at least one embodiment, input data can be received to interface layer 608 and directed to inference module 618, although a different system or service can be used as well. In at least one embodiment, inference module 618 can obtain an appropriate trained network, such as a trained deep neural network (DNN) as discussed herein, from model repository 616 if not already stored locally to inference module 618. Inference module 618 can provide data as input to a trained network, which can then generate one or more inferences as output. This may include, for example, a classification of an instance of input data. In at least one embodiment, inferences can then be transmitted to client device 602 for display or other communication to a user. In at least one embodiment, context data for a user may also be stored to a user context data repository 622, which may include data about a user which may be useful as input to a network in generating inferences, or determining data to return to a user after obtaining instances. In at least one embodiment, relevant data, which may include at least some of input or inference data, may also be stored to a local database 620 for processing future requests. In at least one embodiment, a user can use account or other information to access resources or functionality of a provider environment. In at least one embodiment, if permitted and available, user data may also be collected and used to further train models, in order to provide more accurate inferences for future requests. In at least one embodiment, requests may be received through a user interface to a machine learning application 626 executing on client device 602, and results displayed through a same interface. A client device can include resources such as a processor 628 and memory 630 for generating a request and processing results or a response, as well as at least one data storage element 632 for storing data for machine learning application 626.

In at least one embodiment a processor 628 (or a processor of training manager 612 or inference module 618) will be a central processing unit (CPU). As mentioned, however, resources in such environments can utilize GPUs to process data for at least certain types of requests. With thousands of cores, GPUs are designed to handle substantial parallel workloads and, therefore, have become popular in deep learning for training neural networks and generating predictions. While use of GPUs for offline builds has enabled faster training of larger and more complex models, generating predictions offline implies that either request-time input features cannot be used or predictions must be generated for all permutations of features and stored in a lookup table to serve real-time requests. If a deep learning framework supports a CPU-mode and a model is small and simple enough to perform a feed-forward on a CPU with a reasonable latency, then a service on a CPU instance could host a model. In this case, training can be done offline on a GPU and inference done in real-time on a CPU. If a CPU approach is not viable, then a service can run on a GPU instance. Because GPUs have different performance and cost characteristics than CPUs, however, running a service that offloads a runtime algorithm to a GPU can require it to be designed differently from a CPU based service.

In at least one embodiment, video data can be provided from client device 602 for enhancement in provider environment 606. In at least one embodiment, video data can be processed for enhancement on client device 602. In at least one embodiment, video data may be streamed from a third party content provider 624 and enhanced by third party provider 624, provider environment 606, or client device 602.

FIG. 7 illustrates a system 700 that can be used to classify data, or generate inferences, in at least one embodiment. In at least one embodiment, both supervised and self-supervised training can be used in at least one embodiment discussed herein. In at least one embodiment, a set of training data 702 (e.g., classified or labeled data) is provided as input to function as training data. In at least one embodiment, training data can include instances of at least one type of object for which a neural network is to be trained, as well as information that identifies that type of object. In at least one embodiment, training data might include a set of images that each includes a representation of a type of object, where each image also includes, or is associated with, a label, metadata, classification, or other piece of information identifying a type of object represented in a respective image. Various other types of data may be used as training data as well, as may include text data, audio data, video data, and so on. In at least one embodiment, training data 702 is provided as training input to a training manager 704. In at least one embodiment, training manager 704 can be a system or service that includes hardware and software, such as one or more computing devices executing a training application, for training a neural network (or other model or algorithm, etc.). In at least one embodiment, training manager 704 receives an instruction or request indicating a type of model to be used for training. In at least one embodiment, a model can be any appropriate statistical model, network, or algorithm useful for such purposes, as may include an artificial neural network, deep learning algorithm, learning classifier, Bayesian network, and so on. In at least one embodiment, training manager 704 can select an initial model, or other untrained model, from an appropriate repository 706 and utilize training data 702 to train a model, generating a trained model 708 (e.g., trained deep neural network) that can be used to classify similar types of data, or generate other such inferences. In at least one embodiment where training data is not used, an appropriate initial model can still be selected for training on input data per training manager 704.

In at least one embodiment, a model can be trained in a number of different ways, as may depend in part upon a type of model selected. In at least one embodiment, a machine learning algorithm can be provided with a set of training data, where a model is a model artifact created by a training process. In at least one embodiment, each instance of training data contains a correct answer (e.g., classification), which can be referred to as a target or target attribute. In at least one embodiment, a learning algorithm finds patterns in training data that map input data attributes to a target, an answer to be predicted, and a machine learning model is output that captures these patterns. In at least one embodiment, a machine learning model can then be used to obtain predictions on new data for which a target is not specified.

In at least one embodiment, a training manager 704 can select from a set of machine learning models including binary classification, multiclass classification, and regression models. In at least one embodiment, a type of model to be used can depend at least in part upon a type of target to be predicted. In at least one embodiment, machine learning models for binary classification problems predict a binary outcome, such as one of two possible classes. In at least one embodiment, a learning algorithm such as logistic regression can be used to train binary classification models. In at least one embodiment, machine learning models for multiclass classification problems allow predictions to be generated for multiple classes, such as to predict one of more than two outcomes. Multinomial logistic regression can be useful for training multiclass models. Machine learning models for regression problems predict a numeric value. Linear regression can be useful for training regression models.

In at least one embodiment, in order to train a machine learning model in accordance with one embodiment, a training manager must determine an input training data source, as well as other information such as a name of a data attribute that contains a target to be predicted, required data transformation instructions, and training parameters to control a learning algorithm. In at least one embodiment, during a training process, a training manager 704 may automatically select an appropriate learning algorithm based on a type of target specified in a training data source. In at least one embodiment, machine learning algorithms can accept parameters used to control certain properties of a training process and of a resulting machine learning model. These are referred to herein as training parameters. In at least one embodiment, if no training parameters are specified, a training manager can utilize default values that are known to work well for a large range of machine learning tasks. Examples of training parameters for which values can be specified include a maximum model size, maximum number of passes over training data, shuffle type, regularization type, learning rate, and regularization amount. Default settings may be specified, with options to adjust values to fine-tune performance.

In at least one embodiment, a maximum model size is a total size, in units of bytes, of patterns that are created during a training of a model. In at least one embodiment, a model may be created of a specified size by default, such as a model of 100 MB. If a training manager is unable to determine enough patterns to fill a model size, a smaller model may be created. If a training manager finds more patterns than will fit into a specified size, a maximum cut-off may be enforced by trimming patterns that least affect a quality of a learned model. Choosing a model size provides for control of a trade-off between a predictive quality of a model and a cost of use. In at least one embodiment, smaller models can cause a training manager to remove many patterns to fit within a maximum size limit, affecting a quality of predictions. In at least one embodiment, larger models may cost more to query for real-time predictions. In at least one embodiment, larger input data sets do not necessarily result in larger models because models store patterns, not input data. In at least one embodiment, if patterns are few and simple, a resulting model will be small. Input data that has a large number of raw attributes(input columns) or derived features (outputs of data transformations) will likely have more patterns found and stored during a training process.

In at least one embodiment, training manager 704 can make multiple passes or iterations over training data to attempt to discover patterns. In at least one embodiment, there may be a default number of passes, such as ten passes, while in at least one embodiment up to a maximum number of passes may be set, such as up to one hundred passes. In at least one embodiment there may be no maximum set, or there may be a convergence criterion or other factor set that will trigger an end to a training process. In at least one embodiment training manager 704 can monitor a quality of patterns (such as for model convergence) during training, and can automatically stop training when there are no more data points or patterns to discover. In at least one embodiment, data sets with only a few observations may require more passes over data to obtain sufficiently high model quality. Larger data sets may contain many similar data points, which can reduce a need for a large number of passes. A potential impact of choosing more data passes over data is that model training can takes longer and cost more in terms of resources and system utilization.

In at least one embodiment training data is shuffled before training, or between passes of training. In at least one embodiment, shuffling is a random or pseudo-random shuffling to generate a truly random ordering, although there may be some constraints in place to ensure that there is no grouping of certain types of data, or shuffled data may be reshuffled if such grouping exists, etc. In at least one embodiment, shuffling changes an order or arrangement in which data is utilized for training so that a training algorithm does not encounter groupings of similar types of data, or a single type of data for too many observations in succession. In at least one embodiment, a model might be trained to predict an object. In at least one embodiment, data might be sorted by object type before uploading. In at least one embodiment, an algorithm can then process data alphabetically by object type, encountering only data for a certain object type first. In at least one embodiment, a model will begin to learn patterns for that type of object. In at least one embodiment, a model will then encounter only data for a second object type, and will try to adjust a model to fit that object type, which can degrade patterns that fit that a first object type. This sudden switch from between object types can produce a model that does not learn how to predict object types accurately. In at least one embodiment, shuffling can be performed in at least one embodiment before a training data set is split into training and evaluation subsets, such that a relatively even distribution of data types is utilized for both stages. In at least one embodiment training manager 704 can automatically shuffle data using, for example, a pseudo-random shuffling technique.

In at least one embodiment, when creating a machine learning model in at least one embodiment, training manager 704 can enable a user to specify settings or apply custom options. In at least one embodiment, a user may specify one or more evaluation settings, indicating a portion of input data to be reserved for evaluating a predictive quality of a machine learning model. In at least one embodiment, a user may specify a policy that indicates which attributes and attribute transformations are available for model training. In at least one embodiment, user may also specify various training parameters that control certain properties of a training process and of a resulting model.

In at least one embodiment, once a training manager has determined that training of a model is complete, such as by using at least one end criterion discussed herein, trained model 708 can be provided for use by a classifier 714 in classifying (or otherwise generating inferences for) validation data 712. In at least one embodiment, this involves a logical transition between a training mode for a model and an inference mode for a model. In at least one embodiment, however, trained model 708 will first be passed to an evaluator 710, which may include an application, process, or service executing on at least one computing resource (e.g., a CPU or GPU of at least one server) for evaluating a quality (or another such aspect) of a trained model. In at least one embodiment, a model is evaluated to determine whether this model will provide at least a minimum acceptable or threshold level of performance in predicting a target on new and future data. If not, training manager 704 can continue to train this model. In at least one embodiment, since future data instances will often have unknown target values, it can be desirable to check an accuracy metric of machine learning on data for which a target answer is known, and use this assessment as a proxy for predictive accuracy on future data.

In at least one embodiment, a model is evaluated using a subset of training data 702 that was provided for training. This subset can be determined using a shuffle and split approach as discussed above. In at least one embodiment, this evaluation data subset will be labeled with a target, and thus can act as a source of ground truth for evaluation. Evaluating a predictive accuracy of a machine learning model with same data that was used for training is not useful, as positive evaluations might be generated for models that remember training data instead of generalizing from it. In at least one embodiment, once training has completed, evaluation data subset is processed using trained model 708 and evaluator 710 can determine accuracy of this model by comparing ground truth data against corresponding output (or predictions/observations) of this model. In at least one embodiment, evaluator 710 in at least one embodiment can provide a summary or performance metric indicating how well predicted and true values match. In at least one embodiment, if a trained model does not satisfy at least a minimum performance criterion, or other such accuracy threshold, then training manager 704 can be instructed to perform further training, or in some instances try training a new or different model. In at least one embodiment, if trained model 708 satisfies relevant criteria, then a trained model can be provided for use by classifier 714.

In at least one embodiment, when creating and training a machine learning model, it can be desirable in at least one embodiment to specify model settings or training parameters that will result in a model capable of making accurate predictions. In at least one embodiment, parameters include a number of passes to be performed (forward and/or backward), regularization or refinement, model size, and shuffle type. In at least one embodiment, selecting model parameter settings that produce a best predictive performance on evaluation data might result in an overfitting of a model. In at least one embodiment, overfitting occurs when a model has memorized patterns that occur in training and evaluation data sources, but has failed to generalize patterns in data. Overfitting often occurs when training data includes all data used in an evaluation. In at least one embodiment, a model that has been over fit may perform well during evaluation, but may fail to make accurate predictions on new or otherwise validation data. In at least one embodiment, to avoid selecting an over fitted model as a best model, a training manager can reserve additional data to validate a performance of a model. For example, training data set might be divided into 60 percent for training, and 40 percent for evaluation or validation, which may be divided into two or more stages. In at least one embodiment, after selecting model parameters that work well for evaluation data, leading to convergence on a subset of validation data, such as half this validation data, a second validation may be executed with a remainder of this validation data to ensure performance of this model. If this model meets expectations on validation data, then this model is not overfitting data. In at least one embodiment, a test set or held-out set may be used for testing parameters. In at least one embodiment, using a second validation or testing step helps to select appropriate model parameters to prevent overfitting. However, holding out more data from a training process for validation makes less data available for training. This may be problematic with smaller data sets as there may not be sufficient data available for training. In at least one embodiment, an approach in such a situation is to perform cross-validation as discussed elsewhere herein.

In at least one embodiment, there are many metrics or insights that can be used to review and evaluate a predictive accuracy of a given model. In at least one embodiment, an evaluation outcome contains a prediction accuracy metric to report on an overall success of a model, as well as visualizations to help explore accuracy of a model beyond a prediction accuracy metric. An outcome can also provide an ability to review impact of setting a score threshold, such as for binary classification, and can generate alerts on criteria to check a validity of an evaluation. A choice of a metric and visualization can depend at least in part upon a type of model being evaluated.

In at least one embodiment, once trained and evaluated satisfactorily, a trained machine learning model can be used to build or support a machine learning application. In one embodiment building a machine learning application is an iterative process that involves a sequence of steps. In at least one embodiment, a core machine learning problem(s) can be framed in terms of what is observed and what answer a model is to predict. In at least one embodiment, data can then be collected, cleaned, and prepared to make data suitable for consumption by machine learning model training algorithms. This data can be visualized and analyzed to run sanity checks to validate a quality of data and to understand data. It might be that raw data (e.g., input variables) and answer data (e.g., a target) are not represented in a way that can be used to train a highly predictive model. Therefore, it may be desirable to construct more predictive input representations or features from raw variables. Resulting features can be fed to a learning algorithm to build models and evaluate a quality of models on data that was held out from model building. A model can then be used to generate predictions of a target answer for new data instances.

In at least one embodiment, in system 700 of FIG. 7, a trained model 710 after evaluation is provided, or made available, to a classifier 714 that is able to use a trained model to process validation data. In at least one embodiment, this may include, for example, data received from users or third parties that are not classified, such as query images that are looking for information about what is represented in those images. In at least one embodiment,validation data can be processed by a classifier using a trained model, and results 716 (such as classifications or predictions) that are produced can be sent back to respective sources or otherwise processed or stored. In at least one embodiment, and where such usage is permitted, these now-classified data instances can be stored to a training data repository, which can be used for further training of trained model 708 by a training manager. In at least one embodiment a model will be continually trained as new data is available, but in at least one embodiment these models will be retrained periodically, such as once a day or week, depending upon factors such as a size of a data set or complexity of a model.

In at least one embodiment, classifier 714 can include appropriate hardware and software for processing validation data 712 using a trained model. In at least one embodiment, a classifier will include one or more computer servers each having one or more graphics processing units (GPUs) that are able to process data. In at least one embodiment, configuration and design of GPUs can make them more desirable to use in processing machine learning data than CPUs or other such components. In at least one embodiment, a trained model in at least one embodiment can be loaded into GPU memory and a received data instance provided to a GPU for processing. GPUs can have a much larger number of cores than CPUs, and GPU cores can also be much less complex. In at least one embodiment, a given GPU may be able to process thousands of data instances concurrently via different hardware threads. In at least one embodiment, a GPU can also be configured to maximize floating point throughput, which can provide significant additional processing advantages for a large data set.

In at least one embodiment, even when using GPUs, accelerators, and other such hardware to accelerate tasks such as training of a model or classification of data using such a model, such tasks can still require significant time, resource allocation, and cost. In at least one embodiment, if a machine learning model is to be trained using 700 passes, and a data set includes 1,000,000 data instances to be used for training, then all million instances would need to be processed for each pass. Different portions of an architecture can also be supported by different types of devices. In at least one embodiment, training may be performed using a set of servers at a logically centralized location, as may be offered as a service, while classification of raw data may be performed by such a service or on a client device, among other such options. These devices may also be owned, operated, or controlled by a same entity or multiple entities.

In at least one embodiment, an example neural network 800 illustrated in FIG. 8 can be trained or otherwise utilized in at least one embodiment. In at least one embodiment, a statistical model is an artificial neural network (ANN) that includes a multiple layers of nodes, including an input layer 802, an output layer 806, and multiple layers 804 of intermediate nodes, often referred to as “hidden” layers, as internal layers and nodes are typically not visible or accessible in neural networks. In at least one embodiment, although only a few intermediate layers are illustrated for purposes of explanation, it should be understood that there is no limit to a number of intermediate layers that can be utilized, and any limit on layers will often be a factor of resources or time required for processed using a model. In at least one embodiment, there can be additional types of models, networks, algorithms, or processes used as well, as may include other numbers or selections of nodes and layers, among other such options. In at least one embodiment, validation data can be processed by layers of a network to generate a set of inferences, or inference scores, which can then be fed to a loss function 808.

In at least one embodiment, all nodes of a given layer are interconnected to all nodes of an adjacent layer. In at least one embodiment, nodes of an intermediate layer will then each be connected to nodes of two adjacent layers. In at least one embodiment, nodes are also referred to as neurons or connected units in some models, and connections between nodes are referred to as edges. Each node can perform a function for inputs received, such as by using a specified function. In at least one embodiment, nodes and edges can obtain different weightings during training, and individual layers of nodes can perform specific types of transformations on received input, where those transformations can also be learned or adjusted during training. In at least one embodiment, learning can be supervised or self-supervised learning, as may depend at least in part upon a type of information contained in a training data set. In at least one embodiment, various types of neural networks can be utilized, as may include a convolutional neural network (CNN) that includes a number of convolutional layers and a set of pooling layers, and have proven to be beneficial for applications such as image recognition. CNNs can also be easier to train than other networks due to a relatively small number of parameters to be determined.

In at least one embodiment, such a complex machine learning model can be trained using various tuning parameters. Choosing parameters, fitting a model, and evaluating a model are parts of a model tuning process, often referred to as hyperparameter optimization. Such tuning can involve introspecting an underlying model or data in at least one embodiment. In a training or production setting, a robust workflow can be important to avoid overfitting of hyperparameters as discussed elsewhere herein. Cross-validation and adding Gaussian noise to a training dataset are techniques that can be useful for avoiding overfitting to any one dataset. For hyperparameter optimization it may be desirable to keep training and validation sets fixed. In at least one embodiment, hyperparameters can be tuned in certain categories, as may include data preprocessing (such as translating words to vectors), CNN architecture definition (for example, filter sizes, number of filters), stochastic gradient descent (SGD) parameters (for example, learning rate), and regularization or refinement (for example, dropout probability), among other such options.

In at least one embodiment, instances of a dataset can be embedded into a lower dimensional space of a certain size during pre-processing. In at least one embodiment, a size of this space is a parameter to be tuned. In at least one embodiment, an architecture of a CNN contains many tunable parameters. A parameter for filter sizes can represent an interpretation of information that corresponds to a size of an instance that will be analyzed. In computational linguistics, this is known as an n-gram size. An example CNN uses three different filter sizes, which represent potentially different n-gram sizes. A number of filters per filter size can correspond to a depth of a filter. Each filter attempts to learn something different from a structure of an instance, such as a sentence structure for textual data. In a convolutional layer, an activation function can be a rectified linear unit and a pooling type set as max pooling. Results can then be concatenated into a single dimensional vector, and a last layer is fully connected onto a two-dimensional output. This corresponds to a binary classification to which an optimization function can be applied. One such function is an implementation of a Root Mean Square (RMS) propagation method of gradient descent, where example hyperparameters can include learning rate, batch size, maximum gradient normal, and epochs. With neural networks, regularization can be an extremely important consideration. In at least one embodiment input data may be relatively sparse. A main hyperparameter in such a situation can be a dropout at a penultimate layer, which represents a proportion of nodes that will not “fire” at each training cycle. An example training process can suggest different hyperparameter configurations based on feedback for a performance of previous configurations. This model can be trained with a proposed configuration, evaluated on a designated validation set, and performance reporting. This process can be repeated to, for example, trade off exploration (learning more about different configurations) and exploitation (leveraging previous knowledge to achieve better results).

As training CNNs can be parallelized and GPU-enabled computing resources can be utilized, multiple optimization strategies can be attempted for different scenarios. A complex scenario allows tuning model architecture and preprocessing and stochastic gradient descent parameters. This expands a model configuration space. In a basic scenario, only preprocessing and stochastic gradient descent parameters are tuned. There can be a greater number of configuration parameters in a complex scenario than in a basic scenario. Tuning in a joint space can be performed using a linear or exponential number of steps, iteration through an optimization loop for models. A cost for such a tuning process can be significantly less than for tuning processes such as random search and grid search, without any significant performance loss.

In at least one embodiment backpropagation can be utilized to calculate a gradient used for determining weights for a neural network. Backpropagation is a form of differentiation, and can be used by a gradient descent optimization algorithm to adjust weights applied to various nodes or neurons as discussed above. Weights can be determined using a gradient of a relevant loss function. Backpropagation can utilize a derivative of a loss function with respect to output generated by a statistical model. As mentioned, various nodes can have associated activation functions that define output of respective nodes. Various activation functions can be used as appropriate, as may include radial basis functions (RBFs) and sigmoids, which can be utilized by various support vector machines (SVMs) for transformation of data. An activation function of an intermediate layer of nodes is referred to herein as an inner product kernel. These functions can include, for example, identity functions, step functions, sigmoidal functions, ramp functions, and so on. Activation functions can also be linear or non-linear, among other such options.

In at least one embodiment, an untrained neural network is trained using a training dataset. In at least one embodiment, training framework is a PyTorch framework, Tensorflow, Boost, Caffe, Microsoft Cognitive Toolkit/CNTK, MXNet, Chainer, Keras, Deeplearning4j, or other training framework. In at least one embodiment training framework trains an untrained neural network and enables it to be trained using processing resources described herein to generate a trained neural network. In at least one embodiment, weights may be chosen randomly or by pre-training using a deep belief network. In at least one embodiment, training may be performed in either a supervised, partially supervised, self-supervised, or unsupervised manner.

In at least one embodiment, untrained neural network is trained using supervised learning, wherein training dataset includes an input paired with a desired output for an input, or where training dataset includes input having a known output and an output of neural network is manually graded. In at least one embodiment, untrained neural network is trained in a supervised manner processes inputs from training dataset and compares resulting outputs against a set of expected or desired outputs. In at least one embodiment, errors are then propagated back through untrained neural network. In at least one embodiment, training framework adjusts weights that control untrained neural network. In at least one embodiment, training framework includes tools to monitor how well untrained neural network is converging towards a model, such as trained neural network, suitable to generating correct answers, such as in result, based on known input data, such as new data. In at least one embodiment, training framework trains untrained neural network repeatedly while adjust weights to refine an output of untrained neural network using a loss function and adjustment algorithm, such as stochastic gradient descent. In at least one embodiment, training framework trains untrained neural network until untrained neural network achieves a desired accuracy. In at least one embodiment, trained neural network can then be deployed to implement any number of machine learning operations.

In at least one embodiment, untrained neural network is trained using self-supervised learning, wherein untrained neural network attempts to train itself using unlabeled data. In at least one embodiment, self-supervised learning training dataset will include input data without any associated output data or “ground truth” data. In at least one embodiment, untrained neural network can learn groupings within training dataset and can determine how individual inputs are related to untrained dataset. In at least one embodiment, self-supervised training can be used to generate a self-organizing map, which is a type of trained neural network capable of performing operations useful in reducing dimensionality of new data. In at least one embodiment, self-supervised training can also be used to perform anomaly detection, which allows identification of data points in a new dataset that deviate from normal patterns of new dataset.

In at least one embodiment, semi-supervised learning may be used, which is a technique in which in training dataset includes a mix of labeled and unlabeled data. In at least one embodiment, training framework may be used to perform incremental learning, such as through transferred learning techniques. In at least one embodiment, incremental learning enables trained neural network to adapt to new data without forgetting knowledge instilled within network during initial training.

Inference and Training Logic

FIG. 9A illustrates inference and/or training logic 915 used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B.

In at least one embodiment, inference and/or training logic 915 may include, without limitation, code and/or data storage 901 to store forward and/or output weight and/or input/output data, and/or other parameters to configure neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, training logic 915 may include, or be coupled to code and/or data storage 901 to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture of a neural network to which the code corresponds. In at least one embodiment, code and/or data storage 901 stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during forward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments. In at least one embodiment, any portion of code and/or data storage 901 may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, any portion of code and/or data storage 901 may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or code and/or data storage 901 may be cache memory, dynamic randomly addressable memory (“DRAM”), static randomly addressable memory (“SRAM”), non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, choice of whether code and/or code and/or data storage 901 is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors.

In at least one embodiment, inference and/or training logic 915 may include, without limitation, a code and/or data storage 905 to store backward and/or output weight and/or input/output data corresponding to neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, code and/or data storage 905 stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during backward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments. In at least one embodiment, training logic 915 may include, or be coupled to code and/or data storage 905 to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs). In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture of a neural network to which the code corresponds. In at least one embodiment, any portion of code and/or data storage 905 may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of code and/or data storage 905 may be internal or external to on one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or data storage 905 may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, choice of whether code and/or data storage 905 is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors.

In at least one embodiment, code and/or data storage 901 and code and/or data storage 905 may be separate storage structures. In at least one embodiment, code and/or data storage 901 and code and/or data storage 905 may be same storage structure. In at least one embodiment, code and/or data storage 901 and code and/or data storage 905 may be partially same storage structure and partially separate storage structures. In at least one embodiment, any portion of code and/or data storage 901 and code and/or data storage 905 may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, inference and/or training logic 915 may include, without limitation, one or more arithmetic logic unit(s) (“ALU(s)”) 910, including integer and/or floating point units, to perform logical and/or mathematical operations based, at least in part on, or indicated by, training and/or inference code (e.g., graph code), a result of which may produce activations (e.g., output values from layers or neurons within a neural network) stored in an activation storage 920 that are functions of input/output and/or weight parameter data stored in code and/or data storage 901 and/or code and/or data storage 905. In at least one embodiment, activations stored in activation storage 920 are generated according to linear algebraic and or matrix-based mathematics performed by ALU(s) 910 in response to performing instructions or other code, wherein weight values stored in code and/or data storage 905 and/or code and/or data storage 901 are used as operands along with other values, such as bias values, gradient information, momentum values, or other parameters or hyperparameters, any or all of which may be stored in code and/or data storage 905 or code and/or data storage 901 or another storage on or off-chip.

In at least one embodiment, ALU(s) 910 are included within one or more processors or other hardware logic devices or circuits, whereas in another embodiment, ALU(s) 910 may be external to a processor or other hardware logic device or circuit that uses them (e.g., a co-processor). In at least one embodiment, ALUs 910 may be included within a processor's execution units or otherwise within a bank of ALUs accessible by a processor's execution units either within same processor or distributed between different processors of different types (e.g., central processing units, graphics processing units, fixed function units, etc.). In at least one embodiment, code and/or data storage 901, code and/or data storage 905, and activation storage 920 may be on same processor or other hardware logic device or circuit, whereas in another embodiment, they may be in different processors or other hardware logic devices or circuits, or some combination of same and different processors or other hardware logic devices or circuits. In at least one embodiment, any portion of activation storage 920 may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. Furthermore, inferencing and/or training code may be stored with other code accessible to a processor or other hardware logic or circuit and fetched and/or processed using a processor's fetch, decode, scheduling, execution, retirement and/or other logical circuits.

In at least one embodiment, activation storage 920 may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, activation storage 920 may be completely or partially within or external to one or more processors or other logical circuits. In at least one embodiment, choice of whether activation storage 920 is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors. In at least one embodiment, inference and/or training logic 915 illustrated in FIG. 9A may be used in conjunction with an application-specific integrated circuit (“ASIC”), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic 915 illustrated in FIG. 9A may be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware or other hardware, such as field programmable gate arrays (“FPGAs”).

FIG. 9B illustrates inference and/or training logic 915, according to at least one or more embodiments. In at least one embodiment, inference and/or training logic 915 may include, without limitation, hardware logic in which computational resources are dedicated or otherwise exclusively used in conjunction with weight values or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, inference and/or training logic 915 illustrated in FIG. 9B may be used in conjunction with an application-specific integrated circuit (ASIC), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic 915 illustrated in FIG. 9B may be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware or other hardware, such as field programmable gate arrays (FPGAs). In at least one embodiment, inference and/or training logic 915 includes, without limitation, code and/or data storage 901 and code and/or data storage 905, which may be used to store code (e.g., graph code), weight values and/or other information, including bias values, gradient information, momentum values, and/or other parameter or hyperparameter information. In at least one embodiment illustrated in FIG. 9B, each of code and/or data storage 901 and code and/or data storage 905 is associated with a dedicated computational resource, such as computational hardware 902 and computational hardware 906, respectively. In at least one embodiment, each of computational hardware 902 and computational hardware 906 comprises one or more ALUs that perform mathematical functions, such as linear algebraic functions, only on information stored in code and/or data storage 901 and code and/or data storage 905, respectively, result of which is stored in activation storage 920.

In at least one embodiment, each of code and/or data storage 901 and 905 and corresponding computational hardware 902 and 906, respectively, correspond to different layers of a neural network, such that resulting activation from one “storage/computational pair 901/902 ” of code and/or data storage 901 and computational hardware 902 is provided as an input to “storage/computational pair 905/906 ” of code and/or data storage 905 and computational hardware 906, in order to mirror conceptual organization of a neural network. In at least one embodiment, each of storage/computational pairs 901/902 and 905/906 may correspond to more than one neural network layer. In at least one embodiment, additional storage/computation pairs (not shown) subsequent to or in parallel with storage computation pairs 901/902 and 905/906 may be included in inference and/or training logic 915.

Data Center

FIG. 10 illustrates an example data center 1000, in which at least one embodiment may be used. In at least one embodiment, data center 1000 includes a data center infrastructure layer 1010, a framework layer 1020, a software layer 1030, and an application layer 1040.

In at least one embodiment, as shown in FIG. 10, data center infrastructure layer 1010 may include a resource orchestrator 1012, grouped computing resources 1014, and node computing resources (“node C.R.s”) 1016(1)-1016(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s 1016(1)-1016(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors(including accelerators, field programmable gate arrays (FPGAs), graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (“NW I/O ”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more node C.R.s from among node C.R.s 1016(1)-1016(N) may be a server having one or more of above-mentioned computing resources.

In at least one embodiment, grouped computing resources 1014 may include separate groupings of node C.R.s housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s within grouped computing resources 1014 may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s including CPUs or processors may grouped within one or more racks to provide compute resources to support one or more workloads. In at least one embodiment, one or more racks may also include any number of power modules, cooling modules, and network switches, in any combination.

In at least one embodiment, resource orchestrator 1012 may configure or otherwise control one or more node C.R.s 1016(1)-1016(N) and/or grouped computing resources 1014. In at least one embodiment, resource orchestrator 1012 may include a software design infrastructure (“SDI”) management entity for data center 1000. In at least one embodiment, resource orchestrator may include hardware, software or some combination thereof

In at least one embodiment, as shown in FIG. 10, framework layer 1020 includes a job scheduler 1022, a configuration manager 1024, a resource manager 1026 and a distributed file system 1028. In at least one embodiment, framework layer 1020 may include a framework to support software 1032 of software layer 1030 and/or one or more application(s) 1042 of application layer 1040. In at least one embodiment, software 1032 or application(s) 1042 may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. In at least one embodiment, framework layer 1020 may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system 1028 for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler 1022 may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center 1000. In at least one embodiment, configuration manager 1024 may be capable of configuring different layers such as software layer 1030 and framework layer 1020 including Spark and distributed file system 1028 for supporting large-scale data processing. In at least one embodiment, resource manager 1026 may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system 1028 and job scheduler 1022. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource 1014 at data center infrastructure layer 1010. In at least one embodiment, resource manager 1026 may coordinate with resource orchestrator 1012 to manage these mapped or allocated computing resources.

In at least one embodiment, software 1032 included in software layer 1030 may include software used by at least portions of node C.R.s 1016(1)-1016(N), grouped computing resources 1014, and/or distributed file system 1028 of framework layer 1020. one or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.

In at least one embodiment, application(s) 1042 included in application layer 1040 may include one or more types of applications used by at least portions of node C.R.s 1016(1)-1016(N), grouped computing resources 1014, and/or distributed file system 1028 of framework layer 1020. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) or other machine learning applications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager 1024, resource manager 1026, and resource orchestrator 1012 may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. In at least one embodiment, self-modifying actions may relieve a data center operator of data center 1000 from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

In at least one embodiment, data center 1000 may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources described above with respect to data center 1000. In at least one embodiment, trained machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to data center 1000 by using weight parameters calculated through one or more training techniques described herein.

In at least one embodiment, data center may use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, or other hardware to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described above may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in system FIG. 10 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

Computer Systems

FIG. 11 is a block diagram illustrating an exemplary computer system, which may be a system with interconnected devices and components, a system-on-a-chip (SOC) or some combination thereof 1100 formed with a processor that may include execution units to execute an instruction, according to at least one embodiment. In at least one embodiment, computer system 1100 may include, without limitation, a component, such as a processor 1102 to employ execution units including logic to perform algorithms for process data, in accordance with present disclosure, such as in embodiment described herein. In at least one embodiment, computer system 1100 may include processors, such as PENTIUM® Processor family, Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel® Nervana™ microprocessors available from Intel Corporation of Santa Clara, Calif., although other systems(including PCs having other microprocessors, engineering workstations, set-top boxes and like) may also be used. In at least one embodiment, computer system 1100 may execute a version of WINDOWS' operating system available from Microsoft Corporation of Redmond, Wash., although other operating systems (UNIX and Linux for example), embedded software, and/or graphical user interfaces, may also be used.

Embodiments may be used in other devices such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, embedded applications may include a microcontroller, a digital signal processor (“DSP”), system on a chip, network computers (“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”) switches, or any other system that may perform one or more instructions in accordance with at least one embodiment.

In at least one embodiment, computer system 1100 may include, without limitation, processor 1102 that may include, without limitation, one or more execution units 1108 to perform machine learning model training and/or inferencing according to techniques described herein. In at least one embodiment, computer system 1100 is a single processor desktop or server system, but in another embodiment computer system 1100 may be a multiprocessor system. In at least one embodiment, processor 1102 may include, without limitation, a complex instruction set computer (“CISC”) microprocessor, a reduced instruction set computing (“RISC”) microprocessor, a very long instruction word (“VLIW”) microprocessor, a processor implementing a combination of instruction sets, or any other processor device, such as a digital signal processor, for example. In at least one embodiment, processor 1102 may be coupled to a processor bus 1110 that may transmit data signals between processor 1102 and other components in computer system 1100.

In at least one embodiment, processor 1102 may include, without limitation, a Level 1 (“L1”) internal cache memory (“cache”) 1104. In at least one embodiment, processor 1102 may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory may reside external to processor 1102. Other embodiments may also include a combination of both internal and external caches depending on particular implementation and needs. In at least one embodiment, register file 1106 may store different types of data in various registers including, without limitation, integer registers, floating point registers, status registers, and instruction pointer register.

In at least one embodiment, execution unit 1108, including, without limitation, logic to perform integer and floating point operations, also resides in processor 1102. In at least one embodiment, processor 1102 may also include a microcode (“ucode”) read only memory (“ROM”) that stores microcode for certain macro instructions. In at least one embodiment, execution unit 1108 may include logic to handle a packed instruction set 1109. In at least one embodiment, by including packed instruction set 1109 in an instruction set of a general-purpose processor 1102, along with associated circuitry to execute instructions, operations used by many multimedia applications may be performed using packed data in a general-purpose processor 1102. In one or more embodiments, many multimedia applications may be accelerated and executed more efficiently by using full width of a processor's data bus for performing operations on packed data, which may eliminate need to transfer smaller units of data across processor's data bus to perform one or more operations one data element at a time.

In at least one embodiment, execution unit 1108 may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, computer system 1100 may include, without limitation, a memory 1120. In at least one embodiment, memory 1120 may be implemented as a Dynamic Random Access Memory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device, flash memory device, or other memory device. In at least one embodiment, memory 1120 may store instruction(s) 1119 and/or data 1121 represented by data signals that may be executed by processor 1102.

In at least one embodiment, system logic chip may be coupled to processor bus 1110 and memory 1120. In at least one embodiment, system logic chip may include, without limitation, a memory controller hub (“MCH”) 1116, and processor 1102 may communicate with MCH 1116 via processor bus 1110. In at least one embodiment, MCH 1116 may provide a high bandwidth memory path 1118 to memory 1120 for instruction and data storage and for storage of graphics commands, data and textures. In at least one embodiment, MCH 1116 may direct data signals between processor 1102, memory 1120, and other components in computer system 1100 and to bridge data signals between processor bus 1110, memory 1120, and a system I/O 1122. In at least one embodiment, system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH 1116 may be coupled to memory 1120 through a high bandwidth memory path 1118 and graphics/video card 1112 may be coupled to MCH 1116 through an Accelerated Graphics Port (“AGP”) interconnect 1114.

In at least one embodiment, computer system 1100 may use system I/O 1122 that is a proprietary hub interface bus to couple MCH 1116 to I/O controller hub (“ICH”) 1130. In at least one embodiment, ICH 1130 may provide direct connections to some I/O devices via a local I/O bus. In at least one embodiment, local I/O bus may include, without limitation, a high-speed I/O bus for connecting peripherals to memory 1120, chipset, and processor 1102. Examples may include, without limitation, an audio controller 1129, a firmware hub (“flash BIOS”) 1128, a wireless transceiver 1126, a data storage 1124, a legacy I/O controller 1123 containing user input and keyboard interfaces 1125, a serial expansion port 1127, such as Universal Serial Bus (“USB”), and a network controller 1134. data storage 1124 may comprise a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device.

In at least one embodiment, FIG. 11 illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments, FIG. 11 may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of computer system 1100 are interconnected using compute express link (CXL) interconnects.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in system FIG. 11 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 12 is a block diagram illustrating an electronic device 1200 for utilizing a processor 1210, according to at least one embodiment. In at least one embodiment, electronic device 1200 may be, for example and without limitation, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop, a tablet, a mobile device, a phone, an embedded computer, or any other suitable electronic device.

In at least one embodiment, system 1200 may include, without limitation, processor 1210 communicatively coupled to any suitable number or kind of components, peripherals, modules, or devices. In at least one embodiment, processor 1210 coupled using a bus or interface, such as a 1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus, a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”) bus, a Serial Advance Technology Attachment (“SATA”) bus, a Universal Serial Bus (“USB”) (versions 1, 2, 3), or a Universal Asynchronous Receiver/Transmitter (“UART”) bus. In at least one embodiment, FIG. 12 illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments, FIG. 12 may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices illustrated in FIG. 12 may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of FIG. 12 are interconnected using compute express link (CXL) interconnects.

In at least one embodiment, FIG. 12 may include a display 1224, a touch screen 1225, a touch pad 1230, a Near Field Communications unit (“NFC”) 1245, a sensor hub 1240, a thermal sensor 1246, an Express Chipset (“EC”) 1235, a Trusted Platform Module (“TPM”) 1238, BIOS/firmware/flash memory (“BIOS, FW Flash”) 1222, a DSP 1260, a drive 1220 such as a Solid State Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local area network unit (“WLAN”) 1250, a Bluetooth unit 1252, a Wireless Wide Area Network unit (“WWAN”) 1256, a Global Positioning System (GPS) 1255, a camera (“USB 3.0 camera”) 1254 such as a USB 3.0 camera, and/or a Low Power Double Data Rate (“LPDDR”) memory unit (“LPDDR3”) 1215 implemented in, for example, LPDDR3 standard. These components may each be implemented in any suitable manner.

In at least one embodiment, other components may be communicatively coupled to processor 1210 through components discussed above. In at least one embodiment, an accelerometer 1241, Ambient Light Sensor (“ALS”) 1242, compass 1243, and a gyroscope 1244 may be communicatively coupled to sensor hub 1240. In at least one embodiment, thermal sensor 1239, a fan 1237, a keyboard 1246, and a touch pad 1230 may be communicatively coupled to EC 1235. In at least one embodiment, speaker 1263, headphones 1264, and microphone (“mic”) 1265 may be communicatively coupled to an audio unit (“audio codec and class d amp”) 1262, which may in turn be communicatively coupled to DSP 1260. In at least one embodiment, audio unit 1264 may include, for example and without limitation, an audio coder/decoder (“codec”) and a classD amplifier. In at least one embodiment, SIM card (“SIM”) 1257 may be communicatively coupled to WWAN unit 1256. In at least one embodiment, components such as WLAN unit 1250 and Bluetooth unit 1252, as well as WWAN unit 1256 may be implemented in a Next Generation Form Factor (“NGFF”).

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in system FIG. 12 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 13 illustrates a computer system 1300, according to at least one embodiment. In at least one embodiment, computer system 1300 is configured to implement various processes and methods described throughout this disclosure.

In at least one embodiment, computer system 1300 comprises, without limitation, at least one central processing unit (“CPU”) 1302 that is connected to a communication bus 1310 implemented using any suitable protocol, such as PCI (“Peripheral Component Interconnect”), peripheral component interconnect express (“PCI-Express”), AGP (“Accelerated Graphics Port”), HyperTransport, or any other bus or point-to-point communication protocol(s). In at least one embodiment, computer system 1300 includes, without limitation, a main memory 1304 and control logic (e.g., implemented as hardware, software, or a combination thereof) and data are stored in main memory 1304 which may take form of random access memory (“RAM”). In at least one embodiment, a network interface subsystem (“network interface”) 1322 provides an interface to other computing devices and networks for receiving data from and transmitting data to other systems from computer system 1300.

In at least one embodiment, computer system 1300, in at least one embodiment, includes, without limitation, input devices 1308, parallel processing system 1312, and display devices 1306 which can be implemented using a conventional cathode ray tube (“CRT”), liquid crystal display (“LCD”), light emitting diode (“LED”), plasma display, or other suitable display technologies. In at least one embodiment, user input is received from input devices 1308 such as keyboard, mouse, touchpad, microphone, and more. In at least one embodiment, each of foregoing modules can be situated on a single semiconductor platform to form a processing system.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in system FIG. 13 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 14 illustrates a computer system 1400, according to at least one embodiment. In at least one embodiment, computer system 1400 includes, without limitation, a computer 1410 and a USB stick 1420. In at least one embodiment, computer 1410 may include, without limitation, any number and type of processor(s) (not shown) and a memory (not shown). In at least one embodiment, computer 1410 includes, without limitation, a server, a cloud instance, a laptop, and a desktop computer.

In at least one embodiment, USB stick 1420 includes, without limitation, a processing unit 1430, a USB interface 1440, and USB interface logic 1450. In at least one embodiment, processing unit 1430 may be any instruction execution system, apparatus, or device capable of executing instructions. In at least one embodiment, processing unit 1430 may include, without limitation, any number and type of processing cores (not shown). In at least one embodiment, processing core 1430 comprises an application specific integrated circuit (“ASIC”) that is optimized to perform any amount and type of operations associated with machine learning. For instance, in at least one embodiment, processing core 1430 is a tensor processing unit (“TPC”) that is optimized to perform machine learning inference operations. In at least one embodiment, processing core 1430 is a vision processing unit (“VPU”) that is optimized to perform machine vision and machine learning inference operations.

In at least one embodiment, USB interface 1440 may be any type of USB connector or USB socket. For instance, in at least one embodiment, USB interface 1440 is a USB 3.0 Type-C socket for data and power. In at least one embodiment, USB interface 1440 is a USB 3.0 Type-A connector. In at least one embodiment, USB interface logic 1450 may include any amount and type of logic that enables processing unit 1430 to interface with or devices (e.g., computer 1410) via USB connector 1440.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in system FIG. 14 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 15A illustrates an exemplary architecture in which a plurality of GPUs 1510-1513 is communicatively coupled to a plurality of multi-core processors 1505-1506 over high-speed links 1540-1543 (e.g., buses, point-to-point interconnects, etc.). In one embodiment, high-speed links 1540-1543 support a communication throughput of 4 GB/s, 30 GB/s, 80 GB/s or higher. Various interconnect protocols may be used including, but not limited to, PCIe 4.0 or 5.0 and NVLink 2.0.

In addition, and in one embodiment, two or more of GPUs 1510-1513 are interconnected over high-speed links 1529-1530, which may be implemented using same or different protocols/links than those used for high-speed links 1540-1543. Similarly, two or more of multi-core processors 1505-1506 may be connected over high speed link 1528 which may be symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s, 120 GB/s or higher. Alternatively, all communication between various system components shown in FIG. 15A may be accomplished using same protocols/links (e.g., over a common interconnection fabric).

In one embodiment, each multi-core processor 1505-1506 is communicatively coupled to a processor memory 1501-1502, via memory interconnects 1526-1527, respectively, and each GPU 1510-1513 is communicatively coupled to GPU memory 1520-1523 over GPU memory interconnects 1550-1553, respectively. Memory interconnects 1526-1527 and 1550-1553 may utilize same or different memory access technologies. By way of example, and not limitation, processor memories 1501-1502 and GPU memories 1520-1523 may be volatile memories such as dynamic random access memories (DRAMs)(including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5, GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatile memories such as 3D)(Point or Nano-Ram. In one embodiment, some portion of processor memories 1501-1502 may be volatile memory and another portion may be non-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

As described below, although various processors 1505-1506 and GPUs 1510-1513 may be physically coupled to a particular memory 1501-1502, 1520-1523, respectively, a unified memory architecture may be implemented in which a same virtual system address space (also referred to as “effective address” space) is distributed among various physical memories. For example, processor memories 1501-1502 may each comprise 64 GB of system memory address space and GPU memories 1520-1523 may each comprise 32 GB of system memory address space (resulting in a total of 256 GB addressable memory in this example).

FIG. 15B illustrates additional details for an interconnection between a multi-core processor 1507 and a graphics acceleration module 1546 in accordance with one exemplary embodiment. Graphics acceleration module 1546 may include one or more GPU chips integrated on a line card which is coupled to processor 1507 via high-speed link 1540. Alternatively, graphics acceleration module 1546 may be integrated on a same package or chip as processor 1507.

In at least one embodiment, illustrated processor 1507 includes a plurality of cores 1560A-1560D, each with a translation lookaside buffer 1561A-1561D and one or more caches 1562A-1562D. In at least one embodiment, cores 1560A-1560D may include various other components for executing instructions and processing data which are not illustrated. Caches 1562A-1562D may comprise level 1 (L1) and level 2 (L2) caches. In addition, one or more shared caches 1556 may be included in caches 1562A-1562D and shared by sets of cores 1560A-1560D. For example, one embodiment of processor 1507 includes 24 cores, each with its own L1 cache, twelve shared L2 caches, and twelve shared L3 caches. In this embodiment, one or more L2 and L3 caches are shared by two adjacent cores. Processor 1507 and graphics acceleration module 1546 connect with system memory 1514, which may include processor memories 1501-1502 of FIG. 15A.

Coherency is maintained for data and instructions stored in various caches 1562A-1562D, 1556 and system memory 1514 via inter-core communication over a coherence bus 1564. For example, each cache may have cache coherency logic/circuitry associated therewith to communicate to over coherence bus 1564 in response to detected reads or writes to particular cache lines. In one implementation, a cache snooping protocol is implemented over coherence bus 1564 to snoop cache accesses.

In one embodiment, a proxy circuit 1525 communicatively couples graphics acceleration module 1546 to coherence bus 1564, allowing graphics acceleration module 1546 to participate in a cache coherence protocol as a peer of cores 1560A-1560D. In particular, an interface 1535 provides connectivity to proxy circuit 1525 over high-speed link 1540(e.g., a PCIe bus, NVLink, etc.) and an interface 1537 connects graphics acceleration module 1546 to link 1540.

In one implementation, an accelerator integration circuit 1536 provides cache management, memory access, context management, and interrupt management services on behalf of a plurality of graphics processing engines 1531, 1532, N of graphics acceleration module 1546. Graphics processing engines 1531, 1532, N may each comprise a separate graphics processing unit (GPU). Alternatively, graphics processing engines 1531, 1532, N may comprise different types of graphics processing engines within a GPU such as graphics execution units, media processing engines (e.g., video encoders/decoders), samplers, and blit engines. In at least one embodiment, graphics acceleration module 1546 may be a GPU with a plurality of graphics processing engines 1531-1532, N or graphics processing engines 1531-1532, N may be individual GPUs integrated on a common package, line card, or chip.

In one embodiment, accelerator integration circuit 1536 includes a memory management unit (MMU) 1539 for performing various memory management functions such as virtual-to-physical memory translations (also referred to as effective-to-real memory translations) and memory access protocols for accessing system memory 1514. MMU 1539 may also include a translation lookaside buffer (TLB) (not shown) for caching virtual/effective to physical/real address translations. In one implementation, a cache 1538 stores commands and data for efficient access by graphics processing engines 1531-1532, N. In one embodiment, data stored in cache 1538 and graphics memories 1533-1534, M is kept coherent with core caches 1562A-1562D, 1556, and system memory 1514. As mentioned above, this may be accomplished via proxy circuit 1525 on behalf of cache 1538 and memories 1533-1534, M (e.g., sending updates to cache 1538 related to modifications/accesses of cache lines on processor caches 1562A-1562D, 1556, and receiving updates from cache 1538).

A set of registers 1545 store context data for threads executed by graphics processing engines 1531-1532, N and a context management circuit 1548 manages thread contexts. For example, context management circuit 1548 may perform save and restore operations to save and restore contexts of various threads during contexts switches (e.g., where a first thread is saved and a second thread is stored so that a second thread can be executed by a graphics processing engine). For example, on a context switch, context management circuit 1548 may store current register values to a designated region in memory (e.g., identified by a context pointer). It may then restore register values when returning to a context. In one embodiment, an interrupt management circuit 1547 receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphics processing engine 1531 are translated to real/physical addresses in system memory 1514 by MMU 1539. One embodiment of accelerator integration circuit 1536 supports multiple (e.g., 4, 8, 16) graphics accelerator modules 1546 and/or other accelerator devices. Graphics accelerator module 1546 may be dedicated to a single application executed on processor 1507 or may be shared between multiple applications. In one embodiment, a virtualized graphics execution environment is presented in which resources of graphics processing engines 1531-1532, N are shared with multiple applications or virtual machines (VMs). In at least one embodiment, resources may be subdivided into “slices” which are allocated to different VMs and/or applications based on processing requirements and priorities associated with VMs and/or applications.

In at least one embodiment, accelerator integration circuit 1536 performs as a bridge to a system for graphics acceleration module 1546 and provides address translation and system memory cache services. In addition, accelerator integration circuit 1536 may provide virtualization facilities for a host processor to manage virtualization of graphics processing engines 1531-1532, N, interrupts, and memory management.

Because hardware resources of graphics processing engines 1531-1532, N are mapped explicitly to a real address space seen by host processor 1507, any host processor can address these resources directly using an effective address value. One function of accelerator integration circuit 1536, in one embodiment, is physical separation of graphics processing engines 1531-1532, N so that they appear to a system as independent units.

In at least one embodiment, one or more graphics memories 1533-1534, M are coupled to each of graphics processing engines 1531-1532, N, respectively. Graphics memories 1533-1534, M store instructions and data being processed by each of graphics processing engines 1531-1532, N. Graphics memories 1533-1534, M may be volatile memories such as DRAMs (including stacked DRAMs), GDDR memory (e.g., GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3D)(Point or Nano-Ram.

In one embodiment, to reduce data traffic over link 1540, biasing techniques are used to ensure that data stored in graphics memories 1533-1534, M is data which will be used most frequently by graphics processing engines 1531-1532, N and preferably not used by cores 1560A-1560D (at least not frequently). Similarly, a biasing mechanism attempts to keep data needed by cores (and preferably not graphics processing engines 1531-1532, N) within caches 1562A-1562D, 1556 of cores and system memory 1514.

FIG. 15C illustrates another exemplary embodiment in which accelerator integration circuit 1536 is integrated within processor 1507. In at least this embodiment, graphics processing engines 1531-1532, N communicate directly over high-speed link 1540 to accelerator integration circuit 1536 via interface 1537 and interface 1535 (which, again, may be utilize any form of bus or interface protocol). Accelerator integration circuit 1536 may perform same operations as those described with respect to FIG. 15B, but potentially at a higher throughput given its close proximity to coherence bus 1564 and caches 1562A-1562D, 1556. At least one embodiment supports different programming models including a dedicated-process programming model (no graphics acceleration module virtualization) and shared programming models (with virtualization), which may include programming models which are controlled by accelerator integration circuit 1536 and programming models which are controlled by graphics acceleration module 1546.

In at least one embodiment, graphics processing engines 1531-1532, N are dedicated to a single application or process under a single operating system. In at least one embodiment, a single application can funnel other application requests to graphics processing engines 1531-1532, N, providing virtualization within a VM/partition.

In at least one embodiment, graphics processing engines 1531-1532, N, may be shared by multiple VM/application partitions. In at least one embodiment, shared models may use a system hypervisor to virtualize graphics processing engines 1531-1532, N to allow access by each operating system. For single-partition systems without a hypervisor, graphics processing engines 1531-1532, N are owned by an operating system. In at least one embodiment, an operating system can virtualize graphics processing engines 1531-1532, N to provide access to each process or application.

In at least one embodiment, graphics acceleration module 1546 or an individual graphics processing engine 1531-1532, N selects a process element using a process handle. In at least one embodiment, process elements are stored in system memory 1514 and are addressable using an effective address to real address translation techniques described herein. In at least one embodiment, a process handle may be an implementation-specific value provided to a host process when registering its context with graphics processing engine 1531-1532, N (that is, calling system software to add a process element to a process element linked list). In at least one embodiment, a lower 16-bits of a process handle may be an offset of a process element within a process element linked list.

FIG. 15D illustrates an exemplary accelerator integration slice 1590. As used herein, a “slice” comprises a specified portion of processing resources of accelerator integration circuit 1536. Application effective address space 1582 within system memory 1514 stores process elements 1583. In one embodiment, process elements 1583 are stored in response to GPU invocations 1581 from applications 1580 executed on processor 1507. A process element 1583 contains process state for corresponding application 1580. A work descriptor (WD) 1584 contained in process element 1583 can be a single job requested by an application or may contain a pointer to a queue of jobs. In at least one embodiment, WD 1584 is a pointer to a job request queue in an application's address space 1582.

Graphics acceleration module 1546 and/or individual graphics processing engines 1531-1532, N can be shared by all or a subset of processes in a system. In at least one embodiment, an infrastructure for setting up process state and sending a WD 1584 to a graphics acceleration module 1546 to start a job in a virtualized environment may be included.

In at least one embodiment, a dedicated-process programming model is implementation-specific. In this model, a single process owns graphics acceleration module 1546 or an individual graphics processing engine 1531. Because graphics acceleration module 1546 is owned by a single process, a hypervisor initializes accelerator integration circuit 1536 for an owning partition and an operating system initializes accelerator integration circuit 1536 for an owning process when graphics acceleration module 1546 is assigned.

In operation, a WD fetch unit 1591 in accelerator integration slice 1590 fetches next WD 1584 which includes an indication of work to be done by one or more graphics processing engines of graphics acceleration module 1546. Data from WD 1584 may be stored in registers 1545 and used by MMU 1539, interrupt management circuit 1547, and/or context management circuit 1548 as illustrated. For example, one embodiment of MMU 1539 includes segment/page walk circuitry for accessing segment/page tables 1586 within OS virtual address space 1585. Interrupt management circuit 1547 may process interrupt events 1592 received from graphics acceleration module 1546. When performing graphics operations, an effective address 1593 generated by a graphics processing engine 1531-1532, N is translated to a real address by MMU 1539.

In one embodiment, a same set of registers 1545 are duplicated for each graphics processing engine 1531-1532, N and/or graphics acceleration module 1546 and may be initialized by a hypervisor or operating system. Each of these duplicated registers may be included in an accelerator integration slice 1590. Exemplary registers that may be initialized by a hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 Real Address (RA) Scheduled Processes Area Pointer 3 Authority Mask Override Register 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector Table Entry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA) Hypervisor Accelerator Utilization Record Pointer 9 Storage Description Register

Exemplary registers that may be initialized by an operating system are shown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and Thread Identification 2 Effective Address (EA) Context Save/Restore Pointer 3 Virtual Address (VA) Accelerator Utilization Record Pointer 4 Virtual Address (VA) Storage Segment Table Pointer 5 Authority Mask 6 Work descriptor

In one embodiment, each WD 1584 is specific to a particular graphics acceleration module 1546 and/or graphics processing engines 1531-1532, N. It contains all information required by a graphics processing engine 1531-1532, N to do work or it can be a pointer to a memory location where an application has set up a command queue of work to be completed.

FIG. 15E illustrates additional details for one exemplary embodiment of a shared model. This embodiment includes a hypervisor real address space 1598 in which a process element list 1599 is stored. Hypervisor real address space 1598 is accessible via a hypervisor 1596 which virtualizes graphics acceleration module engines for operating system 1595.

In at least one embodiment, shared programming models allow for all or a subset of processes from all or a subset of partitions in a system to use a graphics acceleration module 1546. There are two programming models where graphics acceleration module 1546 is shared by multiple processes and partitions: time-sliced shared and graphics-directed shared.

In this model, system hypervisor 1596 owns graphics acceleration module 1546 and makes its function available to all operating systems 1595. For a graphics acceleration module 1546 to support virtualization by system hypervisor 1596, graphics acceleration module 1546 may adhere to the following: 1) An application's job request must be autonomous (that is, state does not need to be maintained between jobs), or graphics acceleration module 1546 must provide a context save and restore mechanism. 2) An application's job request is guaranteed by graphics acceleration module 1546 to complete in a specified amount of time, including any translation faults, or graphics acceleration module 1546 provides an ability to preempt processing of a job. 3) Graphics acceleration module 1546 must be guaranteed fairness between processes when operating in a directed shared programming model.

In at least one embodiment, application 1580 is required to make an operating system 1595 system call with a graphics acceleration module 1546 type, a work descriptor (WD), an authority mask register (AMR) value, and a context save/restore area pointer (CSRP). In at least one embodiment, graphics acceleration module 1546 type describes a targeted acceleration function for a system call. In at least one embodiment, graphics acceleration module 1546 type may be a system-specific value. In at least one embodiment, WD is formatted specifically for graphics acceleration module 1546 and can be in a form of a graphics acceleration module 1546 command, an effective address pointer to a user-defined structure, an effective address pointer to a queue of commands, or any other data structure to describe work to be done by graphics acceleration module 1546. In one embodiment, an AMR value is an AMR state to use for a current process. In at least one embodiment, a value passed to an operating system is similar to an application setting an AMR. If accelerator integration circuit 1536 and graphics acceleration module 1546 implementations do not support a User Authority Mask Override Register (UAMOR), an operating system may apply a current UAMOR value to an AMR value before passing an AMR in a hypervisor call. Hypervisor 1596 may optionally apply a current Authority Mask Override Register (AMOR) value before placing an AMR into process element 1583. In at least one embodiment, CSRP is one of registers 1545 containing an effective address of an area in an application's effective address space 1582 for graphics acceleration module 1546 to save and restore context state. This pointer is optional if no state is required to be saved between jobs or when a job is preempted. In at least one embodiment, context save/restore area may be pinned system memory.

Upon receiving a system call, operating system 1595 may verify that application 1580 has registered and been given authority to use graphics acceleration module 1546. Operating system 1595 then calls hypervisor 1596 with information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 An Authority Mask Register (AMR) value (potentially masked) 3 An effective address (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID (PID) and optional thread ID (TID) 5 A virtual address (VA) accelerator utilization record pointer (AURP) 6 Virtual address of storage segment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving a hypervisor call, hypervisor 1596 verifies that operating system 1595 has registered and been given authority to use graphics acceleration module 1546. Hypervisor 1596 then puts process element 1583 into a process element linked list for a corresponding graphics acceleration module 1546 type. A process element may include information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 An Authority Mask Register (AMR) value (potentially masked). 3 An effective address (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID (PID) and optional thread ID (TID) 5 A virtual address (VA) accelerator utilization record pointer (AURP) 6 Virtual address of storage segment table pointer (SSTP) 7 A logical interrupt service number (LISN) 8 Interrupt vector table, derived from hypervisor call parameters 9 A state register (SR) value 10 A logical partition ID (LPID) 11 A real address (RA) hypervisor accelerator utilization record pointer 12 Storage Descriptor Register (SDR)

In at least one embodiment, hypervisor initializes a plurality of accelerator integration slice 1590 registers 1545.

As illustrated in FIG. 15F, in at least one embodiment, a unified memory is used, addressable via a common virtual memory address space used to access physical processor memories 1501-1502 and GPU memories 1520-1523. In this implementation, operations executed on GPUs 1510-1513 utilize a same virtual/effective memory address space to access processor memories 1501-1502 and vice versa, thereby simplifying programmability. In one embodiment, a first portion of a virtual/effective address space is allocated to processor memory 1501, a second portion to second processor memory 1502, a third portion to GPU memory 1520, and so on. In at least one embodiment, an entire virtual/effective memory space (sometimes referred to as an effective address space) is thereby distributed across each of processor memories 1501-1502 and GPU memories 1520-1523, allowing any processor or GPU to access any physical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 1594A-1594 E within one or more of MMUs 1539A-1539 E ensures cache coherence between caches of one or more host processors (e.g., 1505) and GPUs 1510-1513 and implements biasing techniques indicating physical memories in which certain types of data should be stored. While multiple instances of bias/coherence management circuitry 1594A-1594 E are illustrated in FIG. 15F, bias/coherence circuitry may be implemented within an MMU of one or more host processors 1505 and/or within accelerator integration circuit 1536.

One embodiment allows GPU-attached memory 1520-1523 to be mapped as part of system memory, and accessed using shared virtual memory (SVM) technology, but without suffering performance drawbacks associated with full system cache coherence. In at least one embodiment, an ability for GPU-attached memory 1520-1523 to be accessed as system memory without onerous cache coherence overhead provides a beneficial operating environment for GPU offload. This arrangement allows host processor 1505 software to setup operands and access computation results, without overhead of tradition I/O DMA data copies. Such traditional copies involve driver calls, interrupts and memory mapped I/O (MMIO) accesses that are all inefficient relative to simple memory accesses. In at least one embodiment, an ability to access GPU attached memory 1520-1523 without cache coherence overheads can be critical to execution time of an offloaded computation. In cases with substantial streaming write memory traffic, for example, cache coherence overhead can significantly reduce an effective write bandwidth seen by a GPU 1510-1513. In at least one embodiment, efficiency of operand setup, efficiency of results access, and efficiency of GPU computation may play a role in determining effectiveness of a GPU offload.

In at least one embodiment, selection of GPU bias and host processor bias is driven by a bias tracker data structure. A bias table may be used, for example, which may be a page-granular structure(i.e., controlled at a granularity of a memory page) that includes 1 or 2 bits per GPU-attached memory page. In at least one embodiment, a bias table may be implemented in a stolen memory range of one or more GPU-attached memories 1520-1523, with or without a bias cache in GPU 1510-1513 (e.g., to cache frequently/recently used entries of a bias table). Alternatively, an entire bias table may be maintained within a GPU.

In at least one embodiment, a bias table entry associated with each access to GPU-attached memory 1520-1523 is accessed prior to actual access to a GPU memory, causing the following operations. First, local requests from GPU 1510-1513 that find their page in GPU bias are forwarded directly to a corresponding GPU memory 1520-1523. Local requests from a GPU that find their page in host bias are forwarded to processor 1505 (e.g., over a high-speed link as discussed above). In one embodiment, requests from processor 1505 that find a requested page in host processor bias complete a request like a normal memory read. Alternatively, requests directed to a GPU-biased page may be forwarded to GPU 1510-1513. In at least one embodiment, a GPU may then transition a page to a host processor bias if it is not currently using a page. In at least one embodiment, bias state of a page can be changed either by a software-based mechanism, a hardware-assisted software-based mechanism, or, for a limited set of cases, a purely hardware-based mechanism.

One mechanism for changing bias state employs an API call (e.g., OpenCL), which, in turn, calls a GPU's device driver which, in turn, sends a message (or enqueues a command descriptor) to a GPU directing it to change a bias state and, for some transitions, perform a cache flushing operation in a host. In at least one embodiment, cache flushing operation is used for a transition from host processor 1505 bias to GPU bias, but is not for an opposite transition.

In one embodiment, cache coherency is maintained by temporarily rendering GPU-biased pages uncacheable by host processor 1505. To access these pages, processor 1505 may request access from GPU 1510 which may or may not grant access right away. Thus, to reduce communication between processor 1505 and GPU 1510 it is beneficial to ensure that GPU-biased pages are those which are required by a GPU but not host processor 1505 and vice versa.

Inference and/or training logic 915 are used to perform one or more embodiments. Details regarding the inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 16 illustrates exemplary integrated circuits and associated graphics processors that may be fabricated using one or more IP cores, according to various embodiments described herein. In addition to what is illustrated, other logic and circuits may be included in at least one embodiment, including additional graphics processors/cores, peripheral interface controllers, or general-purpose processor cores.

FIG. 16 is a block diagram illustrating an exemplary system on a chip integrated circuit 1600 that may be fabricated using one or more IP cores, according to at least one embodiment. In at least one embodiment, integrated circuit 1600 includes one or more application processor(s) 1605 (e.g., CPUs), at least one graphics processor 1610, and may additionally include an image processor 1615 and/or a video processor 1620, any of which may be a modular IP core. In at least one embodiment, integrated circuit 1600 includes peripheral or bus logic including a USB controller 1625, UART controller 1630, an SPI/SDIO controller 1635, and an I²S/I²C controller 1640. In at least one embodiment, integrated circuit 1600 can include a display device 1645 coupled to one or more of a high-definition multimedia interface (HDMI) controller 1650 and a mobile industry processor interface (MIPI) display interface 1655. In at least one embodiment, storage may be provided by a flash memory subsystem 1660 including flash memory and a flash memory controller. In at least one embodiment, memory interface may be provided via a memory controller 1665 for access to SDRAM or SRAM memory devices. In at least one embodiment, some integrated circuits additionally include an embedded security engine 1670.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in integrated circuit 1600 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIGS. 17A-17B illustrate exemplary integrated circuits and associated graphics processors that may be fabricated using one or more IP cores, according to various embodiments described herein. In addition to what is illustrated, other logic and circuits may be included in at least one embodiment, including additional graphics processors/cores, peripheral interface controllers, or general-purpose processor cores.

FIGS. 17A-17B are block diagrams illustrating exemplary graphics processors for use within an SoC, according to embodiments described herein. FIG. 17A illustrates an exemplary graphics processor 1710 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to at least one embodiment. FIG. 17B illustrates an additional exemplary graphics processor 1740 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to at least one embodiment. In at least one embodiment, graphics processor 1710 of FIG. 17A is a low power graphics processor core. In at least one embodiment, graphics processor 1740 of FIG. 17B is a higher performance graphics processor core. In at least one embodiment, each of graphics processors 1710, 1740 can be variants of graphics processor 1610 of FIG. 16.

In at least one embodiment, graphics processor 1710 includes a vertex processor 1705 and one or more fragment processor(s) 1715A-1715N (e.g., 1715A, 1715B, 1715C, 1715D, through 1715N-1, and 1715N). In at least one embodiment, graphics processor 1710 can execute different shader programs via separate logic, such that vertex processor 1705 is optimized to execute operations for vertex shader programs, while one or more fragment processor(s) 1715A-1715N execute fragment (e.g., pixel) shading operations for fragment or pixel shader programs. In at least one embodiment, vertex processor 1705 performs a vertex processing stage of a 3D graphics pipeline and generates primitives and vertex data. In at least one embodiment, fragment processor(s) 1715A-1715N use primitive and vertex data generated by vertex processor 1705 to produce a framebuffer that is displayed on a display device. In at least one embodiment, fragment processor(s) 1715A-1715N are optimized to execute fragment shader programs as provided for in an OpenGL API, which may be used to perform similar operations as a pixel shader program as provided for in a Direct 3D API.

In at least one embodiment, graphics processor 1710 additionally includes one or more memory management units (MMUs) 1720A-1720B, cache(s) 1725A-1725B, and circuit interconnect(s) 1730A-1730B. In at least one embodiment, one or more MMU(s) 1720A-1720B provide for virtual to physical address mapping for graphics processor 1710, including for vertex processor 1705 and/or fragment processor(s) 1715A-1715N, which may reference vertex or image/texture data stored in memory, in addition to vertex or image/texture data stored in one or more cache(s) 1725A-1725B. In at least one embodiment, one or more MMU(s) 1720A-1720B may be synchronized with other MMUs within system, including one or more MMUs associated with one or more application processor(s) 1605, image processors 1615, and/or video processors 1620 of FIG. 16, such that each processor 1605-1620 can participate in a shared or unified virtual memory system. In at least one embodiment, one or more circuit interconnect(s) 1730A-1730B enable graphics processor 1710 to interface with other IP cores within SoC, either via an internal bus of SoC or via a direct connection.

In at least one embodiment, graphics processor 1740 includes one or more MMU(s) 1720A-1720B, cache(s) 1725A-1725B, and circuit interconnect(s) 1730A-1730B of graphics processor 1710 of FIG. 17A. In at least one embodiment, graphics processor 1740 includes one or more shader core(s) 1755A-1755N (e.g., 1755 A, 1755B, 1755C, 1755D, 1755E, 1755F, through 1755N-1, and 1755N), which provides for a unified shader core architecture in which a single core or type or core can execute all types of programmable shader code, including shader program code to implement vertex shaders, fragment shaders, and/or compute shaders. In at least one embodiment, a number of shader cores can vary. In at least one embodiment, graphics processor 1740 includes an inter-core task manager 1745, which acts as a thread dispatcher to dispatch execution threads to one or more shader cores 1755A-1755N and a tiling unit 1758 to accelerate tiling operations for tile-based rendering, in which rendering operations for a scene are subdivided in image space, for example to exploit local spatial coherence within a scene or to optimize use of internal caches.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in integrated circuit 17A and/or 17B for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIGS. 18A-18B illustrate additional exemplary graphics processor logic according to embodiments described herein. FIG. 18A illustrates a graphics core 1800 that may be included within graphics processor 1610 of FIG. 16, in at least one embodiment, and may be a unified shader core 1755A-1755N as in FIG. 17B in at least one embodiment. FIG. 18B illustrates a highly-parallel general-purpose graphics processing unit 1830 suitable for deployment on a multi-chip module in at least one embodiment.

In at least one embodiment, graphics core 1800 includes a shared instruction cache 1802, a texture unit 1818, and a cache/shared memory 1820 that are common to execution resources within graphics core 1800. In at least one embodiment, graphics core 1800 can include multiple slices 1801A-1801N or partition for each core, and a graphics processor can include multiple instances of graphics core 1800. Slices 1801A-1801N can include support logic including a local instruction cache 1804A-1804N, a thread scheduler 1806A-1806N, a thread dispatcher 1808A-1808N, and a set of registers 1810A-1810N. In at least one embodiment, slices 1801A-1801N can include a set of additional function units (AFUs 1812A-1812N), floating-point units (FPU 1814A-1814N), integer arithmetic logic units (ALUs 1816-1816N), address computational units (ACU 1813A-1813N), double-precision floating-point units (DPFPU 1815A-1815N), and matrix processing units (MPU 1817A-1817N).

In at least one embodiment, FPUs 1814A-1814N can perform single-precision (32-bit) and half-precision (16-bit) floating point operations, while DPFPUs 1815A-1815N perform double precision (64-bit) floating point operations. In at least one embodiment, ALUs 1816A-1816N can perform variable precision integer operations at 8-bit, 16-bit, and 32-bit precision, and can be configured for mixed precision operations. In at least one embodiment, MPUs 1817A-1817N can also be configured for mixed precision matrix operations, including half-precision floating point and 8-bit integer operations. In at least one embodiment, MPUs 1817A-1817N can perform a variety of matrix operations to accelerate machine learning application frameworks, including enabling support for accelerated general matrix to matrix multiplication (GEMM). In at least one embodiment, AFUs 1812A-1812N can perform additional logic operations not supported by floating-point or integer units, including trigonometric operations (e.g., Sine, Cosine, etc.).

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in graphics core 1800 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 18B illustrates a general-purpose processing unit (GPGPU) 1830 that can be configured to enable highly-parallel compute operations to be performed by an array of graphics processing units, in at least one embodiment. In at least one embodiment, GPGPU 1830 can be linked directly to other instances of GPGPU 1830 to create a multi-GPU cluster to improve training speed for deep neural networks. In at least one embodiment, GPGPU 1830 includes a host interface 1832 to enable a connection with a host processor. In at least one embodiment, host interface 1832 is a PCI Express interface. In at least one embodiment, host interj ace 1832 can be a vendor specific communications interface or communications fabric. In at least one embodiment, GPGPU 1830 receives commands from a host processor and uses a global scheduler 1834 to distribute execution threads associated with those commands to a set of compute clusters 1836A-1836H. In at least one embodiment, compute clusters 1836A-1836H share a cache memory 1838. In at least one embodiment, cache memory 1838 can serve as a higher-level cache for cache memories within compute clusters 1836A-1836H.

In at least one embodiment, GPGPU 1830 includes memory 1844A-1844B coupled with compute clusters 1836A-1836H via a set of memory controllers 1842A-1842B. In at least one embodiment, memory 1844A-1844B can include various types of memory devices including dynamic random access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), including graphics double data rate (GDDR) memory.

In at least one embodiment, compute clusters 1836A-1836H each include a set of graphics cores, such as graphics core 1800 of FIG. 18A, which can include multiple types of integer and floating point logic units that can perform computational operations at a range of precisions including suited for machine learning computations. For example, in at least one embodiment, at least a subset of floating point units in each of compute clusters 1836A-1836H can be configured to perform 16-bit or 32-bit floating point operations, while a different subset of floating point units can be configured to perform 64-bit floating point operations.

In at least one embodiment, multiple instances of GPGPU 1830 can be configured to operate as a compute cluster. In at least one embodiment, communication used by compute clusters 1836A-1836H for synchronization and data exchange varies across embodiments. In at least one embodiment, multiple instances of GPGPU 1830 communicate over host interface 1832. In at least one embodiment, GPGPU 1830 includes an I/O hub 1839 that couples GPGPU 1830 with a GPU link 1840 that enables a direct connection to other instances of GPGPU 1830. In at least one embodiment, GPU link 1840 is coupled to a dedicated GPU-to-GPU bridge that enables communication and synchronization between multiple instances of GPGPU 1830. In at least one embodiment, GPU link 1840 couples with a high speed interconnect to transmit and receive data to other GPGPUs or parallel processors. In at least one embodiment, multiple instances of GPGPU 1830 are located in separate data processing systems and communicate via a network device that is accessible via host interface 1832. In at least one embodiment GPU, link 1840 can be configured to enable a connection to a host processor in addition to or as an alternative to host interface 1832.

In at least one embodiment, GPGPU 1830 can be configured to train neural networks. In at least one embodiment, GPGPU 1830 can be used within a inferencing platform. In at least one embodiment, in which GPGPU 1830 is used for inferencing, GPGPU may include fewer compute clusters 1836A-1836H relative to when GPGPU is used for training a neural network. In at least one embodiment, memory technology associated with memory 1844A-1844B may differ between inferencing and training configurations, with higher bandwidth memory technologies devoted to training configurations. In at least one embodiment, inferencing configuration of GPGPU 1830 can support inferencing specific instructions. For example, in at least one embodiment, an inferencing configuration can provide support for one or more 8-bit integer dot product instructions, which may be used during inferencing operations for deployed neural networks.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in GPGPU 1830 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 19 is a block diagram illustrating a computing system 1900 according to at least one embodiment. In at least one embodiment, computing system 1900 includes a processing subsystem 1901 having one or more processor(s) 1902 and a system memory 1904 communicating via an interconnection path that may include a memory hub 1905. In at least one embodiment, memory hub 1905 may be a separate component within a chipset component or may be integrated within one or more processor(s) 1902. In at least one embodiment, memory hub 1905 couples with an I/O subsystem 1911 via a communication link 1906. In at least one embodiment, I/O subsystem 1911 includes an I/O hub 1907 that can enable computing system 1900 to receive input from one or more input device(s) 1908. In at least one embodiment, I/O hub 1907 can enable a display controller, which may be included in one or more processor(s) 1902, to provide outputs to one or more display device(s) 1910A. In at least one embodiment, one or more display device(s) 1910A coupled with I/O hub 1907 can include a local, internal, or embedded display device.

In at least one embodiment, processing subsystem 1901 includes one or more parallel processor(s) 1912 coupled to memory hub 1905 via a bus or other communication link 1913. In at least one embodiment, communication link 1913 may be one of any number of standards based communication link technologies or protocols, such as, but not limited to PCI Express, or may be a vendor specific communications interface or communications fabric. In at least one embodiment, one or more parallel processor(s) 1912 form a computationally focused parallel or vector processing system that can include a large number of processing cores and/or processing clusters, such as a many integrated core (MIC) processor. In at least one embodiment, one or more parallel processor(s) 1912 form a graphics processing subsystem that can output pixels to one of one or more display device(s) 1910A coupled via I/O Hub 1907. In at least one embodiment, one or more parallel processor(s) 1912 can also include a display controller and display interface (not shown) to enable a direct connection to one or more display device(s) 1910B.

In at least one embodiment, a system storage unit 1914 can connect to I/O hub 1907 to provide a storage mechanism for computing system 1900. In at least one embodiment, an I/O switch 1916 can be used to provide an interface mechanism to enable connections between I/O hub 1907 and other components, such as a network adapter 1918 and/or wireless network adapter 1919 that may be integrated into a platform(s), and various other devices that can be added via one or more add-in device(s) 1920. In at least one embodiment, network adapter 1918 can be an Ethernet adapter or another wired network adapter. In at least one embodiment, wireless network adapter 1919 can include one or more of a Wi-Fi, Bluetooth, near field communication (NFC), or other network device that includes one or more wireless radios.

In at least one embodiment, computing system 1900 can include other components not explicitly shown, including USB or other port connections, optical storage drives, video capture devices, and so on, may also be connected to I/O hub 1907. In at least one embodiment, communication paths interconnecting various components in FIG. 19 may be implemented using any suitable protocols, such as PCI (Peripheral Component Interconnect) based protocols (e.g., PCI-Express), or other bus or point-to-point communication interfaces and/or protocol(s), such as NV-Link high-speed interconnect, or interconnect protocols.

In at least one embodiment, one or more parallel processor(s) 1912 incorporate circuitry optimized for graphics and video processing, including, for example, video output circuitry, and constitutes a graphics processing unit (GPU). In at least one embodiment, one or more parallel processor(s) 1912 incorporate circuitry optimized for general purpose processing. In at least one embodiment, components of computing system 1900 may be integrated with one or more other system elements on a single integrated circuit. For example, in at least one embodiment, one or more parallel processor(s) 1912, memory hub 1905, processor(s) 1902, and I/O hub 1907 can be integrated into a system on chip (SoC) integrated circuit. In at least one embodiment, components of computing system 1900 can be integrated into a single package to form a system in package (SIP) configuration. In at least one embodiment, at least a portion of components of computing system 1900 can be integrated into a multi-chip module (MCM), which can be interconnected with other multi-chip modules into a modular computing system.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in system FIG. 1900 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

Processors

FIG. 20A illustrates a parallel processor 2000 according to at least one embodiment. In at least one embodiment, various components of parallel processor 2000 may be implemented using one or more integrated circuit devices, such as programmable processors, application specific integrated circuits (ASICs), or field programmable gate arrays (FPGA). In at least one embodiment, illustrated parallel processor 2000 is a variant of one or more parallel processor(s) 1912 shown in FIG. 19 according to an exemplary embodiment.

In at least one embodiment, parallel processor 2000 includes a parallel processing unit 2002. In at least one embodiment, parallel processing unit 2002 includes an I/O unit 2004 that enables communication with other devices, including other instances of parallel processing unit 2002. In at least one embodiment, I/O unit 2004 may be directly connected to other devices. In at least one embodiment, I/O unit 2004 connects with other devices via use of a hub or switch interface, such as memory hub 1905. In at least one embodiment, connections between memory hub 1905 and I/O unit 2004 form a communication link 1913. In at least one embodiment, I/O unit 2004 connects with a host interface 2006 and a memory crossbar 2016, where host interface 2006 receives commands directed to performing processing operations and memory crossbar 2016 receives commands directed to performing memory operations.

In at least one embodiment, when host interface 2006 receives a command buffer via I/O unit 2004, host interface 2006 can direct work operations to perform those commands to a front end 2008. In at least one embodiment, front end 2008 couples with a scheduler 2010, which is configured to distribute commands or other work items to a processing cluster array 2012. In at least one embodiment, scheduler 2010 ensures that processing cluster array 2012 is properly configured and in a valid state before tasks are distributed to processing cluster array 2012. In at least one embodiment, scheduler 2010 is implemented via firmware logic executing on a microcontroller. In at least one embodiment, microcontroller implemented scheduler 2010 is configurable to perform complex scheduling and work distribution operations at coarse and fine granularity, enabling rapid preemption and context switching of threads executing on processing array 2012. In at least one embodiment, host software can prove workloads for scheduling on processing array 2012 via one of multiple graphics processing doorbells. In at least one embodiment, workloads can then be automatically distributed across processing array 2012 by scheduler 2010 logic within a microcontroller including scheduler 2010.

In at least one embodiment, processing cluster array 2012 can include up to “N” processing clusters (e.g., cluster 2014 A, cluster 2014B, through cluster 2014N). In at least one embodiment, each cluster 2014A-2014N of processing cluster array 2012 can execute a large number of concurrent threads. In at least one embodiment, scheduler 2010 can allocate work to clusters 2014A-2014N of processing cluster array 2012 using various scheduling and/or work distribution algorithms, which may vary depending on workload arising for each type of program or computation. In at least one embodiment, scheduling can be handled dynamically by scheduler 2010, or can be assisted in part by compiler logic during compilation of program logic configured for execution by processing cluster array 2012. In at least one embodiment, different clusters 2014A-2014N of processing cluster array 2012 can be allocated for processing different types of programs or for performing different types of computations.

In at least one embodiment, processing cluster array 2012 can be configured to perform various types of parallel processing operations. In at least one embodiment, processing cluster array 2012 is configured to perform general-purpose parallel compute operations. For example, in at least one embodiment, processing cluster array 2012 can include logic to execute processing tasks including filtering of video and/or audio data, performing modeling operations, including physics operations, and performing data transformations.

In at least one embodiment, processing cluster array 2012 is configured to perform parallel graphics processing operations. In at least one embodiment, processing cluster array 2012 can include additional logic to support execution of such graphics processing operations, including, but not limited to texture sampling logic to perform texture operations, as well as tessellation logic and other vertex processing logic. In at least one embodiment, processing cluster array 2012 can be configured to execute graphics processing related shader programs such as, but not limited to vertex shaders, tessellation shaders, geometry shaders, and pixel shaders. In at least one embodiment, parallel processing unit 2002 can transfer data from system memory via I/O unit 2004 for processing. In at least one embodiment, during processing, transferred data can be stored to on-chip memory (e.g., parallel processor memory 2022) during processing, then written back to system memory.

In at least one embodiment, when parallel processing unit 2002 is used to perform graphics processing, scheduler 2010 can be configured to divide a processing workload into approximately equal sized tasks, to better enable distribution of graphics processing operations to multiple clusters 2014A-2014N of processing cluster array 2012. In at least one embodiment, portions of processing cluster array 2012 can be configured to perform different types of processing. For example, in at least one embodiment, a first portion may be configured to perform vertex shading and topology generation, a second portion may be configured to perform tessellation and geometry shading, and a third portion may be configured to perform pixel shading or other screen space operations, to produce a rendered image for display. In at least one embodiment, intermediate data produced by one or more of clusters 2014A-2014N may be stored in buffers to allow intermediate data to be transmitted between clusters 2014A-2014N for further processing.

In at least one embodiment, processing cluster array 2012 can receive processing tasks to be executed via scheduler 2010, which receives commands defining processing tasks from front end 2008. In at least one embodiment, processing tasks can include indices of data to be processed, e.g., surface (patch) data, primitive data, vertex data, and/or pixel data, as well as state parameters and commands defining how data is to be processed (e.g., what program is to be executed). In at least one embodiment, scheduler 2010 may be configured to fetch indices corresponding to tasks or may receive indices from front end 2008. In at least one embodiment, front end 2008 can be configured to ensure processing cluster array 2012 is configured to a valid state before a workload specified by incoming command buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

In at least one embodiment, each of one or more instances of parallel processing unit 2002 can couple with parallel processor memory 2022. In at least one embodiment, parallel processor memory 2022 can be accessed via memory crossbar 2016, which can receive memory requests from processing cluster array 2012 as well as I/O unit 2004. In at least one embodiment, memory crossbar 2016 can access parallel processor memory 2022 via a memory interface 2018. In at least one embodiment, memory interface 2018 can include multiple partition units (e.g., partition unit 2020A, partition unit 2020B, through partition unit 2020N) that can each couple to a portion (e.g., memory unit) of parallel processor memory 2022. In at least one embodiment, a number of partition units 2020A-2020N is configured to be equal to a number of memory units, such that a first partition unit 2020A has a corresponding first memory unit 2024 A, a second partition unit 2020B has a corresponding memory unit 2024B, and a Nth partition unit 2020N has a corresponding Nth memory unit 2024N. In at least one embodiment, a number of partition units 2020A-2020N may not be equal to a number of memory devices.

In at least one embodiment, memory units 2024A-2024N can include various types of memory devices, including dynamic random access memory (DRAM) or graphics random access memory, such as synchronous graphics random access memory (SGRAM), including graphics double data rate (GDDR) memory. In at least one embodiment, memory units 2024A-2024N may also include 3D stacked memory, including but not limited to high bandwidth memory (HBM). In at least one embodiment, render targets, such as frame buffers or texture maps may be stored across memory units 2024A-2024N, allowing partition units 2020A-2020N to write portions of each render target in parallel to efficiently use available bandwidth of parallel processor memory 2022. In at least one embodiment, a local instance of parallel processor memory 2022 may be excluded in favor of a unified memory design that utilizes system memory in conjunction with local cache memory.

In at least one embodiment, any one of clusters 2014A-2014N of processing cluster array 2012 can process data that will be written to any of memory units 2024A-2024N within parallel processor memory 2022. In at least one embodiment, memory crossbar 2016 can be configured to transfer an output of each cluster 2014A-2014N to any partition unit 2020A-2020N or to another cluster 2014A-2014N, which can perform additional processing operations on an output. In at least one embodiment, each cluster 2014A-2014N can communicate with memory interface 2018 through memory crossbar 2016 to read from or write to various external memory devices. In at least one embodiment, memory crossbar 2016 has a connection to memory interface 2018 to communicate with I/O unit 2004, as well as a connection to a local instance of parallel processor memory 2022, enabling processing units within different processing clusters 2014A-2014N to communicate with system memory or other memory that is not local to parallel processing unit 2002. In at least one embodiment, memory crossbar 2016 can use virtual channels to separate traffic streams between clusters 2014A-2014N and partition units 2020A-2020N.

In at least one embodiment, multiple instances of parallel processing unit 2002 can be provided on a single add-in card, or multiple add-in cards can be interconnected. In at least one embodiment, different instances of parallel processing unit 2002 can be configured to inter-operate even if different instances have different numbers of processing cores, different amounts of local parallel processor memory, and/or other configuration differences. For example, in at least one embodiment, some instances of parallel processing unit 2002 can include higher precision floating point units relative to other instances. In at least one embodiment, systems incorporating one or more instances of parallel processing unit 2002 or parallel processor 2000 can be implemented in a variety of configurations and form factors, including but not limited to desktop, laptop, or handheld personal computers, servers, workstations, game consoles, and/or embedded systems.

FIG. 20B is a block diagram of a partition unit 2020 according to at least one embodiment. In at least one embodiment, partition unit 2020 is an instance of one of partition units 2020A-2020N of FIG. 20A. In at least one embodiment, partition unit 2020 includes an L2 cache 2021, a frame buffer interface 2025, and a raster operations unit (“ROP”) 2026. L2 cache 2021 is a read/write cache that is configured to perform load and store operations received from memory crossbar 2016 and ROP 2026. In at least one embodiment, read misses and urgent write-back requests are output by L2 cache 2021 to frame buffer interface 2025 for processing. In at least one embodiment, updates can also be sent to a frame buffer via frame buffer interface 2025 for processing. In at least one embodiment, frame buffer interface 2025 interfaces with one of memory units in parallel processor memory, such as memory units 2024A-2024N of FIG. 20 (e.g., within parallel processor memory 2022).

In at least one embodiment, ROP 2026 is a processing unit that performs raster operations such as stencil, z test, blending, and so forth. In at least one embodiment, ROP 2026 then outputs processed graphics data that is stored in graphics memory. In at least one embodiment, ROP 2026 includes compression logic to compress depth or color data that is written to memory and decompress depth or color data that is read from memory. In at least one embodiment, compression logic can be lossless compression logic that makes use of one or more of multiple compression algorithms. Compression logic that is performed by ROP 2026 can vary based on statistical characteristics of data to be compressed. For example, in at least one embodiment, delta color compression is performed on depth and color data on a per-tile basis.

In at least one embodiment, ROP 2026 is included within each processing cluster (e.g., cluster 2014A-2014N of FIG. 20A) instead of within partition unit 2020. In at least one embodiment, read and write requests for pixel data are transmitted over memory crossbar 2016 instead of pixel fragment data. In at least one embodiment, processed graphics data may be displayed on a display device, such as one of one or more display device(s) 1910 of FIG. 19, routed for further processing by processor(s) 1902, or routed for further processing by one of processing entities within parallel processor 2000 of FIG. 20A.

FIG. 20C is a block diagram of a processing cluster 2014 within a parallel processing unit according to at least one embodiment. In at least one embodiment, a processing cluster is an instance of one of processing clusters 2014A-2014N of FIG. 20A. In at least one embodiment, one of more of processing cluster(s) 2014 can be configured to execute many threads in parallel, where “thread” refers to an instance of a particular program executing on a particular set of input data. In at least one embodiment, single-instruction, multiple-data (SIMD) instruction issue techniques are used to support parallel execution of a large number of threads without providing multiple independent instruction units. In at least one embodiment, single-instruction, multiple-thread (SIMT) techniques are used to support parallel execution of a large number of generally synchronized threads, using a common instruction unit configured to issue instructions to a set of processing engines within each one of processing clusters.

In at least one embodiment, operation of processing cluster 2014 can be controlled via a pipeline manager 2032 that distributes processing tasks to SIMT parallel processors. In at least one embodiment, pipeline manager 2032 receives instructions from scheduler 2010 of FIG. 20A and manages execution of those instructions via a graphics multiprocessor 2034 and/or a texture unit 2036. In at least one embodiment, graphics multiprocessor 2034 is an exemplary instance of a SIMT parallel processor. However, in at least one embodiment, various types of SIMT parallel processors of differing architectures may be included within processing cluster 2014. In at least one embodiment, one or more instances of graphics multiprocessor 2034 can be included within a processing cluster 2014. In at least one embodiment, graphics multiprocessor 2034 can process data and a data crossbar 2040 can be used to distribute processed data to one of multiple possible destinations, including other shader units. In at least one embodiment, pipeline manager 2032 can facilitate distribution of processed data by specifying destinations for processed data to be distributed vis data crossbar 2040.

In at least one embodiment, each graphics multiprocessor 2034 within processing cluster 2014 can include an identical set of functional execution logic (e.g., arithmetic logic units, load-store units, etc.). In at least one embodiment, functional execution logic can be configured in a pipelined manner in which new instructions can be issued before previous instructions are complete. In at least one embodiment, functional execution logic supports a variety of operations including integer and floating point arithmetic, comparison operations, Boolean operations, bit-shifting, and computation of various algebraic functions. In at least one embodiment, same functional-unit hardware can be leveraged to perform different operations and any combination of functional units may be present.

In at least one embodiment, instructions transmitted to processing cluster 2014 constitute a thread. In at least one embodiment, a set of threads executing across a set of parallel processing engines is a thread group. In at least one embodiment, thread group executes a program on different input data. In at least one embodiment, each thread within a thread group can be assigned to a different processing engine within a graphics multiprocessor 2034. In at least one embodiment, a thread group may include fewer threads than a number of processing engines within graphics multiprocessor 2034. In at least one embodiment, when a thread group includes fewer threads than a number of processing engines, one or more processing engines may be idle during cycles in which that thread group is being processed. In at least one embodiment, a thread group may also include more threads than a number of processing engines within graphics multiprocessor 2034. In at least one embodiment, when a thread group includes more threads than processing engines within graphics multiprocessor 2034, processing can be performed over consecutive clock cycles. In at least one embodiment, multiple thread groups can be executed concurrently on a graphics multiprocessor 2034.

In at least one embodiment, graphics multiprocessor 2034 includes an internal cache memory to perform load and store operations. In at least one embodiment, graphics multiprocessor 2034 can forego an internal cache and use a cache memory (e.g., L1 cache 2048) within processing cluster 2014. In at least one embodiment, each graphics multiprocessor 2034 also has access to L2 caches within partition units (e.g., partition units 2020A-2020N of FIG. 20A) that are shared among all processing clusters 2014 and may be used to transfer data between threads. In at least one embodiment, graphics multiprocessor 2034 may also access off-chip global memory, which can include one or more of local parallel processor memory and/or system memory. In at least one embodiment, any memory external to parallel processing unit 2002 may be used as global memory. In at least one embodiment, processing cluster 2014 includes multiple instances of graphics multiprocessor 2034 can share common instructions and data, which may be stored in L1 cache 2048.

In at least one embodiment, each processing cluster 2014 may include a memory management unit (“MMU”) 2045 that is configured to map virtual addresses into physical addresses. In at least one embodiment, one or more instances of MMU 2045 may reside within memory interface 2018 of FIG. 20A. In at least one embodiment, MMU 2045 includes a set of page table entries (PTEs) used to map a virtual address to a physical address of a tile and optionally a cache line index. In at least one embodiment, MMU 2045 may include address translation lookaside buffers (TLB) or caches that may reside within graphics multiprocessor 2034 or L1 cache or processing cluster 2014. In at least one embodiment, physical address is processed to distribute surface data access locality to allow efficient request interleaving among partition units. In at least one embodiment, cache line index may be used to determine whether a request for a cache line is a hit or miss.

In at least one embodiment, a processing cluster 2014 may be configured such that each graphics multiprocessor 2034 is coupled to a texture unit 2036 for performing texture mapping operations, e.g., determining texture sample positions, reading texture data, and filtering texture data. In at least one embodiment, texture data is read from an internal texture L1 cache (not shown) or from an L1 cache within graphics multiprocessor 2034 and is fetched from an L2 cache, local parallel processor memory, or system memory, as needed. In at least one embodiment, each graphics multiprocessor 2034 outputs processed tasks to data crossbar 2040 to provide processed task(s) to another processing cluster 2014 for further processing or to store processed task(s) in an L2 cache, local parallel processor memory, or system memory via memory crossbar 2016. In at least one embodiment, preROP 2042 (pre-raster operations unit) is configured to receive data from graphics multiprocessor 2034, direct data to ROP units, which may be located with partition units as described herein (e.g., partition units 2020A-2020N of FIG. 20A). In at least one embodiment, PreROP 2042 unit can perform optimizations for color blending, organize pixel color data, and perform address translations.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in graphics processing cluster 2014 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 20D shows a graphics multiprocessor 2034 according to at least one embodiment. In at least one embodiment, graphics multiprocessor 2034 couples with pipeline manager 2032 of processing cluster 2014. In at least one embodiment, graphics multiprocessor 2034 has an execution pipeline including but not limited to an instruction cache 2052, an instruction unit 2054, an address mapping unit 2056, a register file 2058, one or more general purpose graphics processing unit (GPGPU) cores 2062, and one or more load/store units 2066. GPGPU core(s) 2062 and load/store unit(s) 2066 are coupled with cache memory 2072 and shared memory 2070 via a memory and cache interconnect 2068.

In at least one embodiment, instruction cache 2052 receives a stream of instructions to execute from pipeline manager 2032. In at least one embodiment, instructions are cached in instruction cache 2052 and dispatched for execution by instruction unit 2054. In at least one embodiment, instruction unit 2054 can dispatch instructions as thread groups (e.g., warps), with each thread group assigned to a different execution unit within GPGPU core(s) 2062. In at least one embodiment, an instruction can access any of a local, shared, or global address space by specifying an address within a unified address space. In at least one embodiment, address mapping unit 2056 can be used to translate addresses in a unified address space into a distinct memory address that can be accessed by load/store unit(s) 2066.

In at least one embodiment, register file 2058 provides a set of registers for functional units of graphics multiprocessor 2034. In at least one embodiment, register file 2058 provides temporary storage for operands connected to data paths of functional units (e.g., GPGPU cores 2062, load/store units 2066) of graphics multiprocessor 2034. In at least one embodiment, register file 2058 is divided between each of functional units such that each functional unit is allocated a dedicated portion of register file 2058. In at least one embodiment, register file 2058 is divided between different warps being executed by graphics multiprocessor 2034.

In at least one embodiment, GPGPU cores 2062 can each include floating point units (FPUs) and/or integer arithmetic logic units (ALUs) that are used to execute instructions of graphics multiprocessor 2034. GPGPU cores 2062 can be similar in architecture or can differ in architecture. In at least one embodiment, a first portion of GPGPU cores 2062 include a single precision FPU and an integer ALU while a second portion of GPGPU cores include a double precision FPU. In at least one embodiment, FPUs can implement IEEE 754-2008 standard for floating point arithmetic or enable variable precision floating point arithmetic. In at least one embodiment, graphics multiprocessor 2034 can additionally include one or more fixed function or special function units to perform specific functions such as copy rectangle or pixel blending operations. In at least one embodiment one or more of GPGPU cores can also include fixed or special function logic.

In at least one embodiment, GPGPU cores 2062 include SIMD logic capable of performing a single instruction on multiple sets of data. In at least one embodiment GPGPU cores 2062 can physically execute SIMD4, SIMD8, and SIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32 instructions. In at least one embodiment, SIMD instructions for GPGPU cores can be generated at compile time by a shader compiler or automatically generated when executing programs written and compiled for single program multiple data (SPMD) or SIMT architectures. In at least one embodiment, multiple threads of a program configured for an SIMT execution model can executed via a single SIMD instruction. For example, in at least one embodiment, eight SIMT threads that perform same or similar operations can be executed in parallel via a single SIMD8 logic unit.

In at least one embodiment, memory and cache interconnect 2068 is an interconnect network that connects each functional unit of graphics multiprocessor 2034 to register file 2058 and to shared memory 2070. In at least one embodiment, memory and cache interconnect 2068 is a crossbar interconnect that allows load/store unit 2066 to implement load and store operations between shared memory 2070 and register file 2058. In at least one embodiment, register file 2058 can operate at a same frequency as GPGPU cores 2062, thus data transfer between GPGPU cores 2062 and register file 2058 is very low latency. In at least one embodiment, shared memory 2070 can be used to enable communication between threads that execute on functional units within graphics multiprocessor 2034. In at least one embodiment, cache memory 2072 can be used as a data cache for example, to cache texture data communicated between functional units and texture unit 2036. In at least one embodiment, shared memory 2070 can also be used as a program managed cache. In at least one embodiment, threads executing on GPGPU cores 2062 can programmatically store data within shared memory in addition to automatically cached data that is stored within cache memory 2072.

In at least one embodiment, a parallel processor or GPGPU as described herein is communicatively coupled to host/processor cores to accelerate graphics operations, machine-learning operations, pattern analysis operations, and various general purpose GPU (GPGPU) functions. In at least one embodiment, GPU may be communicatively coupled to host processor/cores over a bus or other interconnect (e.g., a high speed interconnect such as PCIe or NVLink). In at least one embodiment, GPU may be integrated on same package or chip as cores and communicatively coupled to cores over an internal processor bus/interconnect(i.e., internal to package or chip). In at least one embodiment, regardless of manner in which GPU is connected, processor cores may allocate work to GPU in form of sequences of commands/instructions contained in a work descriptor. In at least one embodiment, GPU then uses dedicated circuitry/logic for efficiently processing these commands/instructions.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in graphics multiprocessor 2034 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 21 illustrates a multi-GPU computing system 11100, according to at least one embodiment. In at least one embodiment, multi-GPU computing system 11100 can include a processor 11102 coupled to multiple general purpose graphics processing units (GPGPUs) 11106A-D via a host interface switch 11104. In at least one embodiment, host interface switch 11104 is a PCI express switch device that couples processor 11102 to a PCI express bus over which processor 11102 can communicate with GPGPUs 11106A-D. GPGPUs 11106A-D can interconnect via a set of high-speed point to point GPU to GPU links 11116. In at least one embodiment, GPU to GPU links 11116 connect to each of GPGPUs 11106A-D via a dedicated GPU link. In at least one embodiment, P2P GPU links 11116 enable direct communication between each of GPGPUs 11106A-D without requiring communication over host interface bus 11104 to which processor 11102 is connected. In at least one embodiment, with GPU-to-GPU traffic directed to P2P GPU links 11116, host interface bus 11104 remains available for system memory access or to communicate with other instances of multi-GPU computing system 11100, for example, via one or more network devices. While in at least one embodiment GPGPUs 11106A-D connect to processor 11102 via host interface switch 11104, in at least one embodiment processor 11102 includes direct support for P2P GPU links 11116 and can connect directly to GPGPUs 11106A-D.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in multi-GPU computing system 11100 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 22 is a block diagram of a graphics processor 2200, according to at least one embodiment. In at least one embodiment, graphics processor 2200 includes a ring interconnect 2202, a pipeline front-end 2204, a media engine 2237, and graphics cores 2280A-2280N. In at least one embodiment, ring interconnect 2202 couples graphics processor 2200 to other processing units, including other graphics processors or one or more general-purpose processor cores. In at least one embodiment, graphics processor 2200 is one of many processors integrated within a multi-core processing system.

In at least one embodiment, graphics processor 2200 receives batches of commands via ring interconnect 2202. In at least one embodiment, incoming commands are interpreted by a command streamer 2203 in pipeline front-end 2204. In at least one embodiment, graphics processor 2200 includes scalable execution logic to perform 3D geometry processing and media processing via graphics core(s) 2280A-2280N. In at least one embodiment, for 3D geometry processing commands, command streamer 2203 supplies commands to geometry pipeline 2236. In at least one embodiment, for at least some media processing commands, command streamer 2203 supplies commands to a video front end 2234, which couples with a media engine 2237. In at least one embodiment, media engine 2237 includes a Video Quality Engine (VQE) 2230 for video and image post-processing and a multi-format encode/decode (MFX) 2233 engine to provide hardware-accelerated media data encode and decode. In at least one embodiment, geometry pipeline 2236 and media engine 2237 each generate execution threads for thread execution resources provided by at least one graphics core 2280A.

In at least one embodiment, graphics processor 2200 includes scalable thread execution resources featuring modular cores 2280A-2280N (sometimes referred to as core slices), each having multiple sub-cores 2250A-2250N, 2260A-2260N (sometimes referred to as core sub-slices). In at least one embodiment, graphics processor 2200 can have any number of graphics cores 2280A through 2280N. In at least one embodiment, graphics processor 2200 includes a graphics core 2280A having at least a first sub-core 2250A and a second sub-core 2260A. In at least one embodiment, graphics processor 2200 is a low power processor with a single sub-core (e.g., 2250A). In at least one embodiment, graphics processor 2200 includes multiple graphics cores 2280A-2280N, each including a set of first sub-cores 2250A-2250N and a set of second sub-cores 2260A-2260N. In at least one embodiment, each sub-core in first sub-cores 2250A-2250N includes at least a first set of execution units 2252A-2252N and media/texture samplers 2254A-2254N. In at least one embodiment, each sub-core in second sub-cores 2260A-2260N includes at least a second set of execution units 2262A-2262N and samplers 2264A-2264N. In at least one embodiment, each sub-core 2250A-2250N, 2260A-2260N shares a set of shared resources 2270A-2270N. In at least one embodiment, shared resources include shared cache memory and pixel operation logic.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, inference and/or training logic 915 may be used in graphics processor 2200 for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 23 is a block diagram illustrating micro-architecture for a processor 2300 that may include logic circuits to perform instructions, according to at least one embodiment. In at least one embodiment, processor 2300 may perform instructions, including x86 instructions, ARM instructions, specialized instructions for application-specific integrated circuits (ASICs), etc. In at least one embodiment, processor 2300 may include registers to store packed data, such as 64-bit wide MMX™ registers in microprocessors enabled with MMX technology from Intel Corporation of Santa Clara, Calif. In at least one embodiment, MMX registers, available in both integer and floating point forms, may operate with packed data elements that accompany single instruction, multiple data (“SIMD”) and streaming SIMD extensions (“SSE”) instructions. In at least one embodiment, 128-bit wide XMM registers relating to SSE2, SSE3, SSE4, AVX, or beyond (referred to generically as “SSEx”) technology may hold such packed data operands. In at least one embodiment, processor 2300 may perform instructions to accelerate machine learning or deep learning algorithms, training, or inferencing.

In at least one embodiment, processor 2300 includes an in-order front end (“front end”) 2301 to fetch instructions to be executed and prepare instructions to be used later in processor pipeline. In at least one embodiment, front end 2301 may include several units. In at least one embodiment, an instruction prefetcher 2326 fetches instructions from memory and feeds instructions to an instruction decoder 2328 which in turn decodes or interprets instructions. For example, in at least one embodiment, instruction decoder 2328 decodes a received instruction into one or more operations called “micro-instructions” or “micro-operations”(also called “micro ops”or “uops”) that machine may execute. In at least one embodiment, instruction decoder 2328 parses instruction into an opcode and corresponding data and control fields that may be used by micro-architecture to perform operations in accordance with at least one embodiment. In at least one embodiment, a trace cache 2330 may assemble decoded uops into program ordered sequences or traces in a uop queue 2334 for execution. In at least one embodiment, when trace cache 2330 encounters a complex instruction, a microcode ROM 2332 provides uops needed to complete operation.

In at least one embodiment, some instructions may be converted into a single micro-op, whereas others need several micro-ops to complete full operation. In at least one embodiment, if more than four micro-ops are needed to complete an instruction, instruction decoder 2328 may access microcode ROM 2332 to perform instruction. In at least one embodiment, an instruction may be decoded into a small number of micro-ops for processing at instruction decoder 2328. In at least one embodiment, an instruction may be stored within microcode ROM 2332 should a number of micro-ops be needed to accomplish operation. In at least one embodiment, trace cache 2330 refers to an entry point programmable logic array (“PLA”) to determine a correct micro-instruction pointer for reading microcode sequences to complete one or more instructions from microcode ROM 2332 in accordance with at least one embodiment. In at least one embodiment, after microcode ROM 2332 finishes sequencing micro-ops for an instruction, front end 2301 of machine may resume fetching micro-ops from trace cache 2330.

In at least one embodiment, out-of-order execution engine (“out of order engine”) 2303 may prepare instructions for execution. In at least one embodiment, out-of-order execution logic has a number of buffers to smooth out and re-order flow of instructions to optimize performance as they go down pipeline and get scheduled for execution. In at least one embodiment, out-of-order execution engine 2303 includes, without limitation, an allocator/register renamer 2340, a memory uop queue 2342, an integer/floating point uop queue 2344, a memory scheduler 2346, a fast scheduler 2302, a slow/general floating point scheduler (“slow/general FP scheduler”) 2304, and a simple floating point scheduler (“simple FP scheduler”) 2306. In at least one embodiment, fast schedule 2302, slow/general floating point scheduler 2304, and simple floating point scheduler 2306 are also collectively referred to herein as “uop schedulers 2302, 2304, 2306.” In at least one embodiment, allocator/register renamer 2340 allocates machine buffers and resources that each uop needs in order to execute. In at least one embodiment, allocator/register renamer 2340 renames logic registers onto entries in a register file. In at least one embodiment, allocator/register renamer 2340 also allocates an entry for each uop in one of two uop queues, memory uop queue 2342 for memory operations and integer/floating point uop queue 2344 for non-memory operations, in front of memory scheduler 2346 and uop schedulers 2302, 2304, 2306. In at least one embodiment, uop schedulers 2302, 2304, 2306 determine when a uop is ready to execute based on readiness of their dependent input register operand sources and availability of execution resources uops need to complete their operation. In at least one embodiment, fast scheduler 2302 of at least one embodiment may schedule on each half of main clock cycle while slow/general floating point scheduler 2304 and simple floating point scheduler 2306 may schedule once per main processor clock cycle. In at least one embodiment, uop schedulers 2302, 2304, 2306 arbitrate for dispatch ports to schedule uops for execution.

In at least one embodiment, execution block 2311 includes, without limitation, an integer register file/bypass network 2308, a floating point register file/bypass network (“FP register file/bypass network”) 2310, address generation units (“AGUs”) 2312 and 2314, fast Arithmetic Logic Units (ALUs) (“fast ALUs”) 2316 and 2318, a slow Arithmetic Logic Unit (“slow ALU”) 2320, a floating point ALU (“FP”) 2322, and a floating point move unit (“FP move”) 2324. In at least one embodiment, integer register file/bypass network 2308 and floating point register file/bypass network 2310 are also referred to herein as “register files 2308, 2310.” In at least one embodiment, AGUs 2312 and 2314, fast ALUs 2316 and 2318, slow ALU 2320, floating point ALU 2322, and floating point move unit 2324 are also referred to herein as “execution units 2312, 2314, 2316, 2318, 2320, 2322, and 2324.” In at least one embodiment, execution block b 11 may include, without limitation, any number(including zero) and type of register files, bypass networks, address generation units, and execution units, in any combination.

In at least one embodiment, register files 2308, 2310 may be arranged between uop schedulers 2302, 2304, 2306, and execution units 2312, 2314, 2316, 2318, 2320, 2322, and 2324. In at least one embodiment, integer register file/bypass network 2308 performs integer operations. In at least one embodiment, floating point register file/bypass network 2310 performs floating point operations. In at least one embodiment, each of register files 2308, 2310 may include, without limitation, a bypass network that may bypass or forward just completed results that have not yet been written into register file to new dependent uops. In at least one embodiment, register files 2308, 2310 may communicate data with each other. In at least one embodiment, integer register file/bypass network 2308 may include, without limitation, two separate register files, one register file for low-order thirty-two bits of data and a second register file for high order thirty-two bits of data. In at least one embodiment, floating point register file/bypass network 2310 may include, without limitation, 128-bit wide entries because floating point instructions typically have operands from 64 to 128 bits in width.

In at least one embodiment, execution units 2312, 2314, 2316, 2318, 2320, 2322, 2324 may execute instructions. In at least one embodiment, register files 2308, 2310 store integer and floating point data operand values that micro-instructions need to execute. In at least one embodiment, processor 2300 may include, without limitation, any number and combination of execution units 2312, 2314, 2316, 2318, 2320, 2322, 2324. In at least one embodiment, floating point ALU 2322 and floating point move unit 2324, may execute floating point, MMX, SIMD, AVX and SSE, or other operations, including specialized machine learning instructions. In at least one embodiment, floating point ALU 2322 may include, without limitation, a 64-bit by 64-bit floating point divider to execute divide, square root, and remainder micro ops. In at least one embodiment, instructions involving a floating point value may be handled with floating point hardware. In at least one embodiment, ALU operations may be passed to fast ALUs 2316, 2318. In at least one embodiment, fast ALUS 2316, 2318 may execute fast operations with an effective latency of half a clock cycle. In at least one embodiment, most complex integer operations go to slow ALU 2320 as slow ALU 2320 may include, without limitation, integer execution hardware for long-latency type of operations, such as a multiplier, shifts, flag logic, and branch processing. In at least one embodiment, memory load/store operations may be executed by AGUS 2312, 2314. In at least one embodiment, fast ALU 2316, fast ALU 2318, and slow ALU 2320 may perform integer operations on 64-bit data operands. In at least one embodiment, fast ALU 2316, fast ALU 2318, and slow ALU 2320 may be implemented to support a variety of data bit sizes including sixteen, thirty-two, 128, 256, etc. In at least one embodiment, floating point ALU 2322 and floating point move unit 2324 may be implemented to support a range of operands having bits of various widths. In at least one embodiment, floating point ALU 2322 and floating point move unit 2324 may operate on 128-bit wide packed data operands in conjunction with SIMD and multimedia instructions.

In at least one embodiment, uop schedulers 2302, 2304, 2306, dispatch dependent operations before parent load has finished executing. In at least one embodiment, as uops may be speculatively scheduled and executed in processor 2300, processor 2300 may also include logic to handle memory misses. In at least one embodiment, if a data load misses in data cache, there may be dependent operations in flight in pipeline that have left scheduler with temporarily incorrect data. In at least one embodiment, a replay mechanism tracks and re-executes instructions that use incorrect data. In at least one embodiment, dependent operations might need to be replayed and independent ones may be allowed to complete. In at least one embodiment, schedulers and replay mechanism of at least one embodiment of a processor may also be designed to catch instruction sequences for text string comparison operations.

In at least one embodiment, term “registers” may refer to on-board processor storage locations that may be used as part of instructions to identify operands. In at least one embodiment, registers may be those that may be usable from outside of processor (from a programmer's perspective). In at least one embodiment, registers might not be limited to a particular type of circuit. Rather, in at least one embodiment, a register may store data, provide data, and perform functions described herein. In at least one embodiment, registers described herein may be implemented by circuitry within a processor using any number of different techniques, such as dedicated physical registers, dynamically allocated physical registers using register renaming, combinations of dedicated and dynamically allocated physical registers, etc. In at least one embodiment, integer registers store 32-bit integer data. A register file of at least one embodiment also contains eight multimedia SIMD registers for packed data.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment portions or all of inference and/or training logic 915 may be incorporated into execution block 2311 and other memory or registers shown or not shown. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs illustrated in execution block 2311. Moreover, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of execution block 2311 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 24 illustrates a deep learning application processor 2400, according to at least one embodiment. In at least one embodiment, deep learning application processor 2400 uses instructions that, if executed by deep learning application processor 2400, cause deep learning application processor 2400 to perform some or all of processes and techniques described throughout this disclosure. In at least one embodiment, deep learning application processor 2400 is an application-specific integrated circuit (ASIC). In at least one embodiment, application processor 2400 performs matrix multiply operations either “hard-wired” into hardware as a result of performing one or more instructions or both. In at least one embodiment, deep learning application processor 2400 includes, without limitation, processing clusters 2410(1)-2410(12), Inter-Chip Links (“ICLs”) 2420(1)-2420(12), Inter-Chip Controllers (“ICCs”) 2430(1)-2430(2), memory controllers (“Mem Ctrlrs”) 2442(1)-2442(4), high bandwidth memory physical layer (“HBM PHY”) 2444(1)-2444(4), a management-controller central processing unit (“management-controller CPU”) 2450, a Serial Peripheral Interface, Inter-Integrated Circuit, and General Purpose Input/Output block (“SPI, I2C, GPIO”), a peripheral component interconnect express controller and direct memory access block (“PCIe Controller and DMA”) 2470, and a sixteen-lane peripheral component interconnect express port (“PCI Express x 16”) 2480.

In at least one embodiment, processing clusters 2410 may perform deep learning operations, including inference or prediction operations based on weight parameters calculated one or more training techniques, including those described herein. In at least one embodiment, each processing cluster 2410 may include, without limitation, any number and type of processors. In at least one embodiment, deep learning application processor 2400 may include any number and type of processing clusters 2400. In at least one embodiment, Inter-Chip Links 2420 are bi-directional. In at least one embodiment, Inter-Chip Links 2420 and Inter-Chip Controllers 2430 enable multiple deep learning application processors 2400 to exchange information, including activation information resulting from performing one or more machine learning algorithms embodied in one or more neural networks. In at least one embodiment, deep learning application processor 2400 may include any number(including zero) and type of ICLs 2420 and ICCs 2430.

In at least one embodiment, HBM2s 2440 provide a total of 32 Gigabytes (GB) of memory. HBM2 2440(i) is associated with both memory controller 2442(i) and HBM PHY 2444(i). In at least one embodiment, any number of HBM2s 2440 may provide any type and total amount of high bandwidth memory and may be associated with any number(including zero) and type of memory controllers 2442 and HBM PHYs 2444. In at least one embodiment, SPI, I2C, GPIO 2460, PCIe Controller and DMA 2470, and/or PCIe 2480 may be replaced with any number and type of blocks that enable any number and type of communication standards in any technically feasible fashion.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, deep learning application processor 2400 is used to train a machine learning model, such as a neural network, to predict or infer information provided to deep learning application processor 2400. In at least one embodiment, deep learning application processor 2400 is used to infer or predict information based on a trained machine learning model (e.g., neural network) that has been trained by another processor or system or by deep learning application processor 2400. In at least one embodiment, processor 2400 may be used to perform one or more neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 25 is a block diagram of a neuromorphic processor 2500, according to at least one embodiment. In at least one embodiment, neuromorphic processor 2500 may receive one or more inputs from sources external to neuromorphic processor 2500. In at least one embodiment, these inputs may be transmitted to one or more neurons 2502 within neuromorphic processor 2500. In at least one embodiment, neurons 2502 and components thereof may be implemented using circuitry or logic, including one or more arithmetic logic units (ALUs). In at least one embodiment, neuromorphic processor 2500 may include, without limitation, thousands or millions of instances of neurons 2502, but any suitable number of neurons 2502 may be used. In at least one embodiment, each instance of neuron 2502 may include a neuron input 2504 and a neuron output 2506. In at least one embodiment, neurons 2502 may generate outputs that may be transmitted to inputs of other instances of neurons 2502. For example, in at least one embodiment, neuron inputs 2504 and neuron outputs 2506 may be interconnected via synapses 2508.

In at least one embodiment, neurons 2502 and synapses 2508 may be interconnected such that neuromorphic processor 2500 operates to process or analyze information received by neuromorphic processor 2500. In at least one embodiment, neurons 2502 may transmit an output pulse (or “fire” or “spike”) when inputs received through neuron input 2504 exceed a threshold. In at least one embodiment, neurons 2502 may sum or integrate signals received at neuron inputs 2504. For example, in at least one embodiment, neurons 2502 may be implemented as leaky integrate-and-fire neurons, wherein if a sum (referred to as a “membrane potential”) exceeds a threshold value, neuron 2502 may generate an output (or “fire”) using a transfer function such as a sigmoid or threshold function. In at least one embodiment, a leaky integrate-and-fire neuron may sum signals received at neuron inputs 2504 into a membrane potential and may also apply a decay factor (or leak) to reduce a membrane potential. In at least one embodiment, a leaky integrate-and-fire neuron may fire if multiple input signals are received at neuron inputs 2504 rapidly enough to exceed a threshold value(i.e., before a membrane potential decays too low to fire). In at least one embodiment, neurons 2502 may be implemented using circuits or logic that receive inputs, integrate inputs into a membrane potential, and decay a membrane potential. In at least one embodiment, inputs may be averaged, or any other suitable transfer function may be used. Furthermore, in at least one embodiment, neurons 2502 may include, without limitation, comparator circuits or logic that generate an output spike at neuron output 2506 when result of applying a transfer function to neuron input 2504 exceeds a threshold. In at least one embodiment, once neuron 2502 fires, it may disregard previously received input information by, for example, resetting a membrane potential to 0 or another suitable default value. In at least one embodiment, once membrane potential is reset to 0, neuron 2502 may resume normal operation after a suitable period of time (or refractory period).

In at least one embodiment, neurons 2502 may be interconnected through synapses 2508. In at least one embodiment, synapses 2508 may operate to transmit signals from an output of a first neuron 2502 to an input of a second neuron 2502. In at least one embodiment, neurons 2502 may transmit information over more than one instance of synapse 2508. In at least one embodiment, one or more instances of neuron output 2506 may be connected, via an instance of synapse 2508, to an instance of neuron input 2504 in same neuron 2502. In at least one embodiment, an instance of neuron 2502 generating an output to be transmitted over an instance of synapse 2508 may be referred to as a “pre-synaptic neuron” with respect to that instance of synapse 2508. In at least one embodiment, an instance of neuron 2502 receiving an input transmitted over an instance of synapse 2508 may be referred to as a “post-synaptic neuron” with respect to that instance of synapse 2508. Because an instance of neuron 2502 may receive inputs from one or more instances of synapse 2508, and may also transmit outputs over one or more instances of synapse 2508, a single instance of neuron 2502 may therefore be both a “pre-synaptic neuron” and “post-synaptic neuron,” with respect to various instances of synapses 2508, in at least one embodiment.

In at least one embodiment, neurons 2502 may be organized into one or more layers. Each instance of neuron 2502 may have one neuron output 2506 that may fan out through one or more synapses 2508 to one or more neuron inputs 2504. In at least one embodiment, neuron outputs 2506 of neurons 2502 in a first layer 2510 may be connected to neuron inputs 2504 of neurons 2502 in a second layer 2512. In at least one embodiment, layer 2510 may be referred to as a “feed-forward layer.” In at least one embodiment, each instance of neuron 2502 in an instance of first layer 2510 may fan out to each instance of neuron 2502 in second layer 2512. In at least one embodiment, first layer 2510 may be referred to as a “fully connected feed-forward layer.” In at least one embodiment, each instance of neuron 2502 in an instance of second layer 2512 may fan out to fewer than all instances of neuron 2502 in a third layer 2514. In at least one embodiment, second layer 2512 may be referred to as a “sparsely connected feed-forward layer.” In at least one embodiment, neurons 2502 in second layer 2512 may fan out to neurons 2502 in multiple other layers, including to neurons 2502 in (same) second layer 2512. In at least one embodiment, second layer 2512 may be referred to as a “recurrent layer.” In at least one embodiment, neuromorphic processor 2500 may include, without limitation, any suitable combination of recurrent layers and feed-forward layers, including, without limitation, both sparsely connected feed-forward layers and fully connected feed-forward layers.

In at least one embodiment, neuromorphic processor 2500 may include, without limitation, a reconfigurable interconnect architecture or dedicated hard wired interconnects to connect synapse 2508 to neurons 2502. In at least one embodiment, neuromorphic processor 2500 may include, without limitation, circuitry or logic that allows synapses to be allocated to different neurons 2502 as needed based on neural network topology and neuron fan-in/out. For example, in at least one embodiment, synapses 2508 may be connected to neurons 2502 using an interconnect fabric, such as network-on-chip, or with dedicated connections. In at least one embodiment, synapse interconnections and components thereof may be implemented using circuitry or logic.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 26 is a block diagram of a processing system, according to at least one embodiment. In at least one embodiment, system 2600 includes one or more processors 2602 and one or more graphics processors 2608, and may be a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors 2602 or processor cores 2607. In at least one embodiment, system 2600 is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices.

In at least one embodiment, system 2600 can include, or be incorporated within a server-based gaming platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In at least one embodiment, system 2600 is a mobile phone, smart phone, tablet computing device or mobile Internet device. In at least one embodiment, processing system 2600 can also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, or virtual reality device. In at least one embodiment, processing system 2600 is a television or set top box device having one or more processors 2602 and a graphical interface generated by one or more graphics processors 2608.

In at least one embodiment, one or more processors 2602 each include one or more processor cores 2607 to process instructions which, when executed, perform operations for system and user software. In at least one embodiment, each of one or more processor cores 2607 is configured to process a specific instruction set 2609. In at least one embodiment, instruction set 2609 may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). In at least one embodiment, processor cores 2607 may each process a different instruction set 2609, which may include instructions to facilitate emulation of other instruction sets. In at least one embodiment, processor core 2607 may also include other processing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor 2602 includes cache memory 2604. In at least one embodiment, processor 2602 can have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory is shared among various components of processor 2602. In at least one embodiment, processor 2602 also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores 2607 using known cache coherency techniques. In at least one embodiment, register file 2606 is additionally included in processor 2602 which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). In at least one embodiment, register file 2606 may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s) 2602 are coupled with one or more interface bus(es) 2610 to transmit communication signals such as address, data, or control signals between processor 2602 and other components in system 2600. In at least one embodiment, interface bus 2610, in one embodiment, can be a processor bus, such as a version of a Direct Media Interface (DMI) bus. In at least one embodiment, interface 2610 is not limited to a DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In at least one embodiment processor(s) 2602 include an integrated memory controller 2616 and a platform controller hub 2630. In at least one embodiment, memory controller 2616 facilitates communication between a memory device and other components of system 2600, while platform controller hub (PCH) 2630 provides connections to I/O devices via a local I/O bus.

In at least one embodiment, memory device 2620 can be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In at least one embodiment memory device 2620 can operate as system memory for system 2600, to store data 2622 and instructions 2621 for use when one or more processors 2602 executes an application or process. In at least one embodiment, memory controller 2616 also couples with an optional external graphics processor 2612, which may communicate with one or more graphics processors 2608 in processors 2602 to perform graphics and media operations. In at least one embodiment, a display device 2611 can connect to processor(s) 2602. In at least one embodiment display device 2611 can include one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, display device 2611 can include a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications.

In at least one embodiment, platform controller hub 2630 enables peripherals to connect to memory device 2620 and processor 2602 via a high-speed I/O bus. In at least one embodiment, I/O peripherals include, but are not limited to, an audio controller 2646, a network controller 2634, a firmware interface 2628, a wireless transceiver 2626, touch sensors 2625, a data storage device 2624 (e.g., hard disk drive, flash memory, etc.). In at least one embodiment, data storage device 2624 can connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, touch sensors 2625 can include touch screen sensors, pressure sensors, or fingerprint sensors. In at least one embodiment, wireless transceiver 2626 can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at least one embodiment, firmware interface 2628 enables communication with system firmware, and can be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, network controller 2634 can enable a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) couples with interface bus 2610. In at least one embodiment, audio controller 2646 is a multi-channel high definition audio controller. In at least one embodiment, system 2600 includes an optional legacy I/O controller 2640 for coupling legacy (e.g., Personal System 2 (PS/2)) devices to system. In at least one embodiment, platform controller hub 2630 can also connect to one or more Universal Serial Bus (USB) controllers 2642 connect input devices, such as keyboard and mouse 2643 combinations, a camera 2644, or other USB input devices.

In at least one embodiment, an instance of memory controller 2616 and platform controller hub 2630 may be integrated into a discreet external graphics processor, such as external graphics processor 2612. In at least one embodiment, platform controller hub 2630 and/or memory controller 2616 may be external to one or more processor(s) 2602. For example, in at least one embodiment, system 2600 can include an external memory controller 2616 and platform controller hub 2630, which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with processor(s) 2602.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment portions or all of inference and/or training logic 915 may be incorporated into graphics processor 2600. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in graphics processor 2612. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated in FIGS. 9A or 9B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor 2600 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 27 is a block diagram of a processor 2700 having one or more processor cores 2702A-2702N, an integrated memory controller 2714, and an integrated graphics processor 2708, according to at least one embodiment. In at least one embodiment, processor 2700 can include additional cores up to and including additional core 2702N represented by dashed lined boxes. In at least one embodiment, each of processor cores 2702A-2702N includes one or more internal cache units 2704A-2704N. In at least one embodiment, each processor core also has access to one or more shared cached units 2706.

In at least one embodiment, internal cache units 2704A-2704N and shared cache units 2706 represent a cache memory hierarchy within processor 2700. In at least one embodiment, cache memory units 2704A-2704N may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where a highest level of cache before external memory is classified as an LLC. In at least one embodiment, cache coherency logic maintains coherency between various cache units 2706 and 2704A-2704N.

In at least one embodiment, processor 2700 may also include a set of one or more bus controller units 2716 and a system agent core 2710. In at least one embodiment, one or more bus controller units 2716 manage a set of peripheral buses, such as one or more PCI or PCI express busses. In at least one embodiment, system agent core 2710 provides management functionality for various processor components. In at least one embodiment, system agent core 2710 includes one or more integrated memory controllers 2714 to manage access to various external memory devices (not shown).

In at least one embodiment, one or more of processor cores 2702A-2702N include support for simultaneous multi-threading. In at least one embodiment, system agent core 2710 includes components for coordinating and operating cores 2702A-2702N during multi-threaded processing. In at least one embodiment, system agent core 2710 may additionally include a power control unit (PCU), which includes logic and components to regulate one or more power states of processor cores 2702A-2702N and graphics processor 2708.

In at least one embodiment, processor 2700 additionally includes graphics processor 2708 to execute graphics processing operations. In at least one embodiment, graphics processor 2708 couples with shared cache units 2706, and system agent core 2710, including one or more integrated memory controllers 2714. In at least one embodiment, system agent core 2710 also includes a display controller 2711 to drive graphics processor output to one or more coupled displays. In at least one embodiment, display controller 2711 may also be a separate module coupled with graphics processor 2708 via at least one interconnect, or may be integrated within graphics processor 2708.

In at least one embodiment, a ring based interconnect unit 2712 is used to couple internal components of processor 2700. In at least one embodiment, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, graphics processor 2708 couples with ring interconnect 2712 via an I/O link 2713.

In at least one embodiment, I/O link 2713 represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module 2718, such as an eDRAM module. In at least one embodiment, each of processor cores 2702A-2702N and graphics processor 2708 use embedded memory modules 2718 as a shared Last Level Cache.

In at least one embodiment, processor cores 2702A-2702N are homogenous cores executing a common instruction set architecture. In at least one embodiment, processor cores 2702A-2702N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores 2702A-2702N execute a common instruction set, while one or more other cores of processor cores 2702A-27-02N executes a subset of a common instruction set or a different instruction set. In at least one embodiment, processor cores 2702A-2702N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In at least one embodiment, processor 2700 can be implemented on one or more chips or as an SoC integrated circuit.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment portions or all of inference and/or training logic 915 may be incorporated into processor 2700. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in graphics processor 2612, graphics core(s) 2702A-2702N, or other components in FIG. 27. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated in FIGS. 9A or 9B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor 2700 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 28 is a block diagram of hardware logic of a graphics processor core 2800, according to at least one embodiment described herein. In at least one embodiment, graphics processor core 2800 is included within a graphics core array. In at least one embodiment, graphics processor core 2800, sometimes referred to as a core slice, can be one or multiple graphics cores within a modular graphics processor. In at least one embodiment, graphics processor core 2800 is exemplary of one graphics core slice, and a graphics processor as described herein may include multiple graphics core slices based on target power and performance envelopes. In at least one embodiment, each graphics core 2800 can include a fixed function block 2830 coupled with multiple sub-cores 2801A-2801F, also referred to as sub-slices, that include modular blocks of general-purpose and fixed function logic.

In at least one embodiment, fixed function block 2830 includes a geometry/fixed function pipeline 2836 that can be shared by all sub-cores in graphics processor 2800, for example, in lower performance and/or lower power graphics processor implementations. In at least one embodiment, geometry/fixed function pipeline 2836 includes a 3D fixed function pipeline, a video front-end unit, a thread spawner and thread dispatcher, and a unified return buffer manager, which manages unified return buffers.

In at least one embodiment fixed, function block 2830 also includes a graphics SoC interface 2837, a graphics microcontroller 2838, and a media pipeline 2839. In at least one embodiment fixed, graphics SoC interface 2837 provides an interface between graphics core 2800 and other processor cores within a system on a chip integrated circuit. In at least one embodiment, graphics microcontroller 2838 is a programmable sub-processor that is configurable to manage various functions of graphics processor 2800, including thread dispatch, scheduling, and pre-emption. In at least one embodiment, media pipeline 2839 includes logic to facilitate decoding, encoding, pre-processing, and/or post-processing of multimedia data, including image and video data. In at least one embodiment, media pipeline 2839 implements media operations via requests to compute or sampling logic within sub-cores 2801-2801F.

In at least one embodiment, SoC interface 2837 enables graphics core 2800 to communicate with general-purpose application processor cores (e.g., CPUs) and/or other components within an SoC, including memory hierarchy elements such as a shared last level cache memory, system RAM, and/or embedded on-chip or on-package DRAM. In at least one embodiment, SoC interface 2837 can also enable communication with fixed function devices within an SoC, such as camera imaging pipelines, and enables use of and/or implements global memory atomics that may be shared between graphics core 2800 and CPUs within an SoC. In at least one embodiment, SoC interface 2837 can also implement power management controls for graphics core 2800 and enable an interface between a clock domain of graphic core 2800 and other clock domains within an SoC. In at least one embodiment, SoC interface 2837 enables receipt of command buffers from a command streamer and global thread dispatcher that are configured to provide commands and instructions to each of one or more graphics cores within a graphics processor. In at least one embodiment, commands and instructions can be dispatched to media pipeline 2839, when media operations are to be performed, or a geometry and fixed function pipeline (e.g., geometry and fixed function pipeline 2836, geometry and fixed function pipeline 2814) when graphics processing operations are to be performed.

In at least one embodiment, graphics microcontroller 2838 can be configured to perform various scheduling and management tasks for graphics core 2800. In at least one embodiment, graphics microcontroller 2838 can perform graphics and/or compute workload scheduling on various graphics parallel engines within execution unit (EU) arrays 2802A-2802F, 2804A-2804F within sub-cores 2801A-2801F. In at least one embodiment, host software executing on a CPU core of an SoC including graphics core 2800 can submit workloads one of multiple graphic processor doorbells, which invokes a scheduling operation on an appropriate graphics engine. In at least one embodiment, scheduling operations include determining which workload to run next, submitting a workload to a command streamer, pre-empting existing workloads running on an engine, monitoring progress of a workload, and notifying host software when a workload is complete. In at least one embodiment, graphics microcontroller 2838 can also facilitate low-power or idle states for graphics core 2800, providing graphics core 2800 with an ability to save and restore registers within graphics core 2800 across low-power state transitions independently from an operating system and/or graphics driver software on a system.

In at least one embodiment, graphics core 2800 may have greater than or fewer than illustrated sub-cores 2801A-2801F, up to N modular sub-cores. For each set of N sub-cores, in at least one embodiment, graphics core 2800 can also include shared function logic 2810, shared and/or cache memory 2812, a geometry/fixed function pipeline 2814, as well as additional fixed function logic 2816 to accelerate various graphics and compute processing operations. In at least one embodiment, shared function logic 2810 can include logic units (e.g., sampler, math, and/or inter-thread communication logic) that can be shared by each N sub-cores within graphics core 2800. In at least one embodiment fixed, shared and/or cache memory 2812 can be a last-level cache for N sub-cores 2801A-2801F within graphics core 2800 and can also serve as shared memory that is accessible by multiple sub-cores. In at least one embodiment, geometry/fixed function pipeline 2814 can be included instead of geometry/fixed function pipeline 2836 within fixed function block 2830 and can include same or similar logic units.

In at least one embodiment, graphics core 2800 includes additional fixed function logic 2816 that can include various fixed function acceleration logic for use by graphics core 2800. In at least one embodiment, additional fixed function logic 2816 includes an additional geometry pipeline for use in position only shading. In position-only shading, at least two geometry pipelines exist, whereas in a full geometry pipeline within geometry/fixed function pipeline 2816, 2836, and a cull pipeline, which is an additional geometry pipeline which may be included within additional fixed function logic 2816. In at least one embodiment, cull pipeline is a trimmed down version of a full geometry pipeline. In at least one embodiment, a full pipeline and a cull pipeline can execute different instances of an application, each instance having a separate context. In at least one embodiment, position only shading can hide long cull runs of discarded triangles, enabling shading to be completed earlier in some instances. For example, in at least one embodiment, cull pipeline logic within additional fixed function logic 2816 can execute position shaders in parallel with a main application and generally generates critical results faster than a full pipeline, as cull pipeline fetches and shades position attribute of vertices, without performing rasterization and rendering of pixels to a frame buffer. In at least one embodiment, cull pipeline can use generated critical results to compute visibility information for all triangles without regard to whether those triangles are culled. In at least one embodiment, full pipeline (which in this instance may be referred to as a replay pipeline) can consume visibility information to skip culled triangles to shade only visible triangles that are finally passed to a rasterization phase.

In at least one embodiment, additional fixed function logic 2816 can also include machine-learning acceleration logic, such as fixed function matrix multiplication logic, for implementations including optimizations for machine learning training or inferencing.

In at least one embodiment, within each graphics sub-core 2801A-2801F includes a set of execution resources that may be used to perform graphics, media, and compute operations in response to requests by graphics pipeline, media pipeline, or shader programs. In at least one embodiment, graphics sub-cores 2801A-2801F include multiple EU arrays 2802A-2802F, 2804A-2804F, thread dispatch and inter-thread communication (TD/IC) logic 2803A-2803F, a 3D (e.g., texture) sampler 2805A-2805F, a media sampler 2806A-2806F, a shader processor 2807A-2807F, and shared local memory (SLM) 2808A-2808 F. EU arrays 2802A-2802F, 2804A-2804F each include multiple execution units, which are general-purpose graphics processing units capable of performing floating-point and integer/fixed-point logic operations in service of a graphics, media, or compute operation, including graphics, media, or compute shader programs. In at least one embodiment, TD/IC logic 2803A-2803F performs local thread dispatch and thread control operations for execution units within a sub-core and facilitate communication between threads executing on execution units of a sub-core. In at least one embodiment, 3D sampler 2805A-2805F can read texture or other 3D graphics related data into memory. In at least one embodiment, 3D sampler can read texture data differently based on a configured sample state and texture format associated with a given texture. In at least one embodiment, media sampler 2806A-2806F can perform similar read operations based on a type and format associated with media data. In at least one embodiment, each graphics sub-core 2801A-2801F can alternately include a unified 3D and media sampler. In at least one embodiment, threads executing on execution units within each of sub-cores 2801A-2801F can make use of shared local memory 2808A-2808F within each sub-core, to enable threads executing within a thread group to execute using a common pool of on-chip memory.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, portions or all of inference and/or training logic 915 may be incorporated into graphics processor 2810. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in graphics processor 2612, graphics microcontroller 2838, geometry & fixed function pipeline 2814 and 2836, or other logic in FIG. 27. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated in FIGS. 9A or 9B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor 2800 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIGS. 29A-29B illustrate thread execution logic 2900 including an array of processing elements of a graphics processor core according to at least one embodiment. FIG. 29A illustrates at least one embodiment, in which thread execution logic 2900 is used. FIG. 29B illustrates exemplary internal details of an execution unit, according to at least one embodiment.

As illustrated in FIG. 29A, in at least one embodiment, thread execution logic 2900 includes a shader processor 2902, a thread dispatcher 2904, instruction cache 2906, a scalable execution unit array including a plurality of execution units 2908A-2908N, sampler(s) 2910, a data cache 2912, and a data port 2914. In at least one embodiment a scalable execution unit array can dynamically scale by enabling or disabling one or more execution units (e.g., any of execution unit 2908A, 2908B, 2908C, 2908D, through 2908N-1 and 2908N) based on computational requirements of a workload, for example. In at least one embodiment, scalable execution units are interconnected via an interconnect fabric that links to each of execution unit. In at least one embodiment, thread execution logic 2900 includes one or more connections to memory, such as system memory or cache memory, through one or more of instruction cache 2906, data port 2914, sampler 2910, and execution units 2908A-2908N. In at least one embodiment, each execution unit (e.g., 2908A) is a stand-alone programmable general-purpose computational unit that is capable of executing multiple simultaneous hardware threads while processing multiple data elements in parallel for each thread. In at least one embodiment, array of execution units 2908A-2908N is scalable to include any number individual execution units.

In at least one embodiment, execution units 2908A-2908N are primarily used to execute shader programs. In at least one embodiment, shader processor 2902 can process various shader programs and dispatch execution threads associated with shader programs via a thread dispatcher 2904. In at least one embodiment, thread dispatcher 2904 includes logic to arbitrate thread initiation requests from graphics and media pipelines and instantiate requested threads on one or more execution units in execution units 2908A-2908N. For example, in at least one embodiment, a geometry pipeline can dispatch vertex, tessellation, or geometry shaders to thread execution logic for processing. In at least one embodiment, thread dispatcher 2904 can also process runtime thread spawning requests from executing shader programs.

In at least one embodiment, execution units 2908A-2908N support an instruction set that includes native support for many standard 3D graphics shader instructions, such that shader programs from graphics libraries (e.g., Direct 3D and OpenGL) are executed with a minimal translation. In at least one embodiment, execution units support vertex and geometry processing (e.g., vertex programs, geometry programs, vertex shaders), pixel processing (e.g., pixel shaders, fragment shaders) and general-purpose processing (e.g., compute and media shaders). In at least one embodiment, each of execution units 2908A-2908N, which include one or more arithmetic logic units (ALUs), is capable of multi-issue single instruction multiple data (SIMD) execution and multi-threaded operation enables an efficient execution environment despite higher latency memory accesses. In at least one embodiment, each hardware thread within each execution unit has a dedicated high-bandwidth register file and associated independent thread-state. In at least one embodiment, execution is multi-issue per clock to pipelines capable of integer, single and double precision floating point operations, SIMD branch capability, logical operations, transcendental operations, and other miscellaneous operations. In at least one embodiment, while waiting for data from memory or one of shared functions, dependency logic within execution units 2908A-2908N causes a waiting thread to sleep until requested data has been returned. In at least one embodiment, while a waiting thread is sleeping, hardware resources may be devoted to processing other threads. For example, in at least one embodiment, during a delay associated with a vertex shader operation, an execution unit can perform operations for a pixel shader, fragment shader, or another type of shader program, including a different vertex shader.

In at least one embodiment, each execution unit in execution units 2908A-2908N operates on arrays of data elements. In at least one embodiment, a number of data elements is “execution size,” or number of channels for an instruction. In at least one embodiment, an execution channel is a logical unit of execution for data element access, masking, and flow control within instructions. In at least one embodiment, a number of channels may be independent of a number of physical Arithmetic Logic Units (ALUs) or Floating Point Units (FPUs) for a particular graphics processor. In at least one embodiment, execution units 2908A-2908N support integer and floating-point data types.

In at least one embodiment, an execution unit instruction set includes SIMD instructions. In at least one embodiment, various data elements can be stored as a packed data type in a register and an execution unit will process various elements based on data size of elements. For example, in at least one embodiment, when operating on a 256-bit wide vector, 256 bits of a vector are stored in a register and an execution unit operates on a vector as four separate 64-bit packed data elements (Quad-Word (QW) size data elements), eight separate 32-bit packed data elements (Double Word (DW) size data elements), sixteen separate 16-bit packed data elements (Word (W) size data elements), or thirty-two separate 8-bit data elements (byte (B) size data elements). However, in at least one embodiment, different vector widths and register sizes are possible.

In at least one embodiment, one or more execution units can be combined into a fused execution unit 2909A-2909N having thread control logic (2907A-2907N) that is common to fused EUs. In at least one embodiment, multiple EUs can be fused into an EU group. In at least one embodiment, each EU in fused EU group can be configured to execute a separate SIMD hardware thread. Number of EUs in a fused EU group can vary according to various embodiments. In at least one embodiment, various SIMD widths can be performed per-EU, including but not limited to SIMD8, SIMD16, and SIMD32. In at least one embodiment, each fused graphics execution unit 2909A-2909N includes at least two execution units. For example, in at least one embodiment, fused execution unit 2909A includes a first EU 2908 A, second EU 2908B, and thread control logic 2907 A that is common to first EU 2908A and second EU 2908B. In at least one embodiment, thread control logic 2907A controls threads executed on fused graphics execution unit 2909A, allowing each EU within fused execution units 2909A-2909N to execute using a common instruction pointer register.

In at least one embodiment, one or more internal instruction caches (e.g., 2906) are included in thread execution logic 2900 to cache thread instructions for execution units. In at least one embodiment, one or more data caches (e.g., 2912) are included to cache thread data during thread execution. In at least one embodiment, a sampler 2910 is included to provide texture sampling for 3D operations and media sampling for media operations. In at least one embodiment, sampler 2910 includes specialized texture or media sampling functionality to process texture or media data during a sampling process before providing sampled data to an execution unit.

During execution, in at least one embodiment, graphics and media pipelines send thread initiation requests to thread execution logic 2900 via thread spawning and dispatch logic. In at least one embodiment, once a group of geometric objects has been processed and rasterized into pixel data, pixel processor logic (e.g., pixel shader logic, fragment shader logic, etc.) within shader processor 2902 is invoked to further compute output information and cause results to be written to output surfaces (e.g., color buffers, depth buffers, stencil buffers, etc.). In at least one embodiment, a pixel shader or fragment shader calculates values of various vertex attributes that are to be interpolated across a rasterized object. In at least one embodiment, pixel processor logic within shader processor 2902 then executes an application programming interface (API)-supplied pixel or fragment shader program . In at least one embodiment, to execute a shader program, shader processor 2902 dispatches threads to an execution unit (e.g., 2908A) via thread dispatcher 2904. In at least one embodiment, shader processor 2902 uses texture sampling logic in sampler 2910 to access texture data in texture maps stored in memory. In at least one embodiment, arithmetic operations on texture data and input geometry data compute pixel color data for each geometric fragment, or discards one or more pixels from further processing.

In at least one embodiment, data port 2914 provides a memory access mechanism for thread execution logic 2900 to output processed data to memory for further processing on a graphics processor output pipeline. In at least one embodiment, data port 2914 includes or couples to one or more cache memories (e.g., data cache 2912) to cache data for memory access via a data port.

As illustrated in FIG. 29B, in at least one embodiment, a graphics execution unit 2908 can include an instruction fetch unit 2937, a general register file array (GRF) 2924, an architectural register file array (ARF) 2926, a thread arbiter 2922, a send unit 2930, a branch unit 2932, a set of SIMD floating point units (FPUs) 2934, and, in at least one embodiment, a set of dedicated integer SIMD ALUs 2935. In at least one embodiment, GRF 2924 and ARF 2926 includes a set of general register files and architecture register files associated with each simultaneous hardware thread that may be active in graphics execution unit 2908. In at least one embodiment, per thread architectural state is maintained in ARF 2926, while data used during thread execution is stored in GRF 2924. In at least one embodiment, execution state of each thread, including instruction pointers for each thread, can be held in thread-specific registers in ARF 2926.

In at least one embodiment, graphics execution unit 2908 has an architecture that is a combination of Simultaneous Multi-Threading (SMT) and fine-grained Interleaved Multi-Threading (IMT). In at least one embodiment, architecture has a modular configuration that can be fine-tuned at design time based on a target number of simultaneous threads and number of registers per execution unit, where execution unit resources are divided across logic used to execute multiple simultaneous threads.

In at least one embodiment, graphics execution unit 2908 can co-issue multiple instructions, which may each be different instructions. In at least one embodiment, thread arbiter 2922 of graphics execution unit thread 2908 can dispatch instructions to one of send unit 2930, branch unit 2942, or SIMD FPU(s) 2934 for execution. In at least one embodiment, each execution thread can access 128 general-purpose registers within GRF 2924, where each register can store 32 bytes, accessible as a SIMD 8-element vector of 32-bit data elements. In at least one embodiment, each execution unit thread has access to 4 Kbytes within GRF 2924, although embodiments are not so limited, and greater or fewer register resources may be provided in other embodiments. In at least one embodiment, up to seven threads can execute simultaneously, although a number of threads per execution unit can also vary according to embodiments. In at least one embodiment, in which seven threads may access 4 Kbytes, GRF 2924 can store a total of 28 Kbytes. In at least one embodiment, flexible addressing modes can permit registers to be addressed together to build effectively wider registers or to represent strided rectangular block data structures.

In at least one embodiment, memory operations, sampler operations, and other longer-latency system communications are dispatched via “send” instructions that are executed by message passing send unit 2930. In at least one embodiment, branch instructions are dispatched to a dedicated branch unit 2932 to facilitate SIMD divergence and eventual convergence.

In at least one embodiment graphics execution unit 2908 includes one or more SIMD floating point units (FPU(s)) 2934 to perform floating-point operations. In at least one embodiment, FPU(s) 2934 also support integer computation. In at least one embodiment FPU(s) 2934 can SIMD execute up to M number of 32-bit floating-point (or integer) operations, or SIMD execute up to 2M 16-bit integer or 16-bit floating-point operations. In at least one embodiment, at least one of FPU(s) provides extended math capability to support high-throughput transcendental math functions and double precision 64-bit floating-point. In at least one embodiment, a set of 8-bit integer SIMD ALUs 2935 are also present, and may be specifically optimized to perform operations associated with machine learning computations.

In at least one embodiment, arrays of multiple instances of graphics execution unit 2908 can be instantiated in a graphics sub-core grouping (e.g., a sub-slice). In at least one embodiment, execution unit 2908 can execute instructions across a plurality of execution channels. In at least one embodiment, each thread executed on graphics execution unit 2908 is executed on a different channel.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, portions or all of inference and/or training logic 915 may be incorporated into execution logic 2900. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated in FIGS. 9A or 9B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of execution logic 2900 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 30 illustrates a parallel processing unit (“PPU”) 3000, according to at least one embodiment. In at least one embodiment, PPU 3000 is configured with machine-readable code that, if executed by PPU 3000, causes PPU 3000 to perform some or all of processes and techniques described throughout this disclosure. In at least one embodiment, PPU 3000 is a multi-threaded processor that is implemented on one or more integrated circuit devices and that utilizes multithreading as a latency-hiding technique designed to process computer-readable instructions (also referred to as machine-readable instructions or simply instructions) on multiple threads in parallel. In at least one embodiment, a thread refers to a thread of execution and is an instantiation of a set of instructions configured to be executed by PPU 3000. In at least one embodiment, PPU 3000 is a graphics processing unit (“GPU”) configured to implement a graphics rendering pipeline for processing three-dimensional (“3D”) graphics data in order to generate two-dimensional (“2D”) image data for display on a display device such as a liquid crystal display (“LCD”) device. In at least one embodiment, PPU 3000 is utilized to perform computations such as linear algebra operations and machine-learning operations. FIG. 30 illustrates an example parallel processor for illustrative purposes only and should be construed as a non-limiting example of processor architectures contemplated within scope of this disclosure and that any suitable processor may be employed to supplement and/or substitute for same.

In at least one embodiment, one or more PPUs 3000 are configured to accelerate High Performance Computing (“HPC”), data center, and machine learning applications. In at least one embodiment, PPU 3000 is configured to accelerate deep learning systems and applications including following non-limiting examples: autonomous vehicle platforms, deep learning, high-accuracy speech, image, text recognition systems, intelligent video analytics, molecular simulations, drug discovery, disease diagnosis, weather forecasting, big data analytics, astronomy, molecular dynamics simulation, financial modeling, robotics, factory automation, real-time language translation, online search optimizations, and personalized user recommendations, and more.

In at least one embodiment, PPU 3000 includes, without limitation, an Input/Output (“I/O”) unit 3006, a front-end unit 3010, a scheduler unit 3012, a work distribution unit 3014, a hub 3016, a crossbar (“Xbar”) 3020, one or more general processing clusters (“GPCs”) 3018, and one or more partition units (“memory partition units”) 3022. In at least one embodiment, PPU 3000 is connected to a host processor or other PPUs 3000 via one or more high-speed GPU interconnects (“GPU interconnects”) 3008. In at least one embodiment, PPU 3000 is connected to a host processor or other peripheral devices via an interconnect 3002. In at least one embodiment, PPU 3000 is connected to a local memory comprising one or more memory devices (“memory”) 3004. In at least one embodiment, memory devices 3004 include, without limitation, one or more dynamic random access memory (“DRAM”) devices. In at least one embodiment, one or more DRAM devices are configured and/or configurable as high-bandwidth memory (“HBM”) subsystems, with multiple DRAM dies stacked within each device.

In at least one embodiment, high-speed GPU interconnect 3008 may refer to a wire-based multi-lane communications link that is used by systems to scale and include one or more PPUs 3000 combined with one or more central processing units (“CPUs”), supports cache coherence between PPUs 3000 and CPUs, and CPU mastering. In at least one embodiment, data and/or commands are transmitted by high-speed GPU interconnect 3008 through hub 3016 to/from other units of PPU 3000 such as one or more copy engines, video encoders, video decoders, power management units, and other components which may not be explicitly illustrated in FIG. 30.

In at least one embodiment, I/O unit 3006 is configured to transmit and receive communications (e.g., commands, data) from a host processor (not illustrated in FIG. 30) over system bus 3002. In at least one embodiment, I/O unit 3006 communicates with host processor directly via system bus 3002 or through one or more intermediate devices such as a memory bridge. In at least one embodiment, I/O unit 3006 may communicate with one or more other processors, such as one or more of PPUs 3000 via system bus 3002. In at least one embodiment, I/O unit 3006 implements a Peripheral Component Interconnect Express (“PCIe”) interface for communications over a PCIe bus. In at least one embodiment, I/O unit 3006 implements interfaces for communicating with external devices.

In at least one embodiment, I/O unit 3006 decodes packets received via system bus 3002. In at least one embodiment, at least some packets represent commands configured to cause PPU 3000 to perform various operations. In at least one embodiment, I/O unit 3006 transmits decoded commands to various other units of PPU 3000 as specified by commands. In at least one embodiment, commands are transmitted to front-end unit 3010 and/or transmitted to hub 3016 or other units of PPU 3000 such as one or more copy engines, a video encoder, a video decoder, a power management unit, etc. (not explicitly illustrated in FIG. 30). In at least one embodiment, I/O unit 3006 is configured to route communications between and among various logical units of PPU 3000.

In at least one embodiment, a program executed by host processor encodes a command stream in a buffer that provides workloads to PPU 3000 for processing. In at least one embodiment, a workload comprises instructions and data to be processed by those instructions. In at least one embodiment, buffer is a region in a memory that is accessible (e.g., read/write) by both host processor and PPU 3000—a host interface unit may be configured to access buffer in a system memory connected to system bus 3002 via memory requests transmitted over system bus 3002 by I/O unit 3006. In at least one embodiment, host processor writes command stream to buffer and then transmits a pointer to start of command stream to PPU 3000 such that front-end unit 3010 receives pointers to one or more command streams and manages one or more command streams, reading commands from command streams and forwarding commands to various units of PPU 3000.

In at least one embodiment, front-end unit 3010 is coupled to scheduler unit 3012 that configures various GPCs 3018 to process tasks defined by one or more command streams. In at least one embodiment, scheduler unit 3012 is configured to track state information related to various tasks managed by scheduler unit 3012 where state information may indicate which of GPCs 3018 a task is assigned to, whether task is active or inactive, a priority level associated with task, and so forth. In at least one embodiment, scheduler unit 3012 manages execution of a plurality of tasks on one or more of GPCs 3018.

In at least one embodiment, scheduler unit 3012 is coupled to work distribution unit 3014 that is configured to dispatch tasks for execution on GPCs 3018. In at least one embodiment, work distribution unit 3014 tracks a number of scheduled tasks received from scheduler unit 3012 and work distribution unit 3014 manages a pending task pool and an active task pool for each of GPCs 3018. In at least one embodiment, pending task pool comprises a number of slots (e.g., 32 slots) that contain tasks assigned to be processed by a particular GPC 3018; active task pool may comprise a number of slots (e.g., 4 slots) for tasks that are actively being processed by GPCs 3018 such that as one of GPCs 3018 completes execution of a task, that task is evicted from active task pool for GPC 3018 and one of other tasks from pending task pool is selected and scheduled for execution on GPC 3018. In at least one embodiment, if an active task is idle on GPC 3018, such as while waiting for a data dependency to be resolved, then active task is evicted from GPC 3018 and returned to pending task pool while another task in pending task pool is selected and scheduled for execution on GPC 3018.

In at least one embodiment, work distribution unit 3014 communicates with one or more GPCs 3018 via XBar 3020. In at least one embodiment, XBar 3020 is an interconnect network that couples many of units of PPU 3000 to other units of PPU 3000 and can be configured to couple work distribution unit 3014 to a particular GPC 3018. In at least one embodiment, one or more other units of PPU 3000 may also be connected to XBar 3020 via hub 3016.

In at least one embodiment, tasks are managed by scheduler unit 3012 and dispatched to one of GPCs 3018 by work distribution unit 3014. GPC 3018 is configured to process task and generate results. In at least one embodiment, results may be consumed by other tasks within GPC 3018, routed to a different GPC 3018 via XBar 3020, or stored in memory 3004. In at least one embodiment, results can be written to memory 3004 via partition units 3022, which implement a memory interface for reading and writing data to/from memory 3004. In at least one embodiment, results can be transmitted to another PPU 3004 or CPU via high-speed GPU interconnect 3008. In at least one embodiment, PPU 3000 includes, without limitation, a number U of partition units 3022 that is equal to number of separate and distinct memory devices 3004 coupled to PPU 3000. In at least one embodiment, partition unit 3022 will be described in more detail below in conjunction with FIG. 32.

In at least one embodiment, a host processor executes a driver kernel that implements an application programming interface (“API”) that enables one or more applications executing on host processor to schedule operations for execution on PPU 3000. In at least one embodiment, multiple compute applications are simultaneously executed by PPU 3000 and PPU 3000 provides isolation, quality of service (“QoS”), and independent address spaces for multiple compute applications. In at least one embodiment, an application generates instructions (e.g., in form of API calls) that cause driver kernel to generate one or more tasks for execution by PPU 3000 and driver kernel outputs tasks to one or more streams being processed by PPU 3000. In at least one embodiment, each task comprises one or more groups of related threads, which may be referred to as a warp. In at least one embodiment, a warp comprises a plurality of related threads (e.g., 32 threads) that can be executed in parallel. In at least one embodiment, cooperating threads can refer to a plurality of threads including instructions to perform task and that exchange data through shared memory. In at least one embodiment, threads and cooperating threads are described in more detail, in accordance with at least one embodiment, in conjunction with FIG. 32.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, deep learning application processor is used to train a machine learning model, such as a neural network, to predict or infer information provided to PPU 3000. In at least one embodiment, PPU 3000 is used to infer or predict information based on a trained machine learning model (e.g., neural network) that has been trained by another processor or system or by PPU 3000. In at least one embodiment, PPU 3000 may be used to perform one or more neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 31 illustrates a general processing cluster (“GPC”) 3100, according to at least one embodiment. In at least one embodiment, GPC 3100 is GPC 3018 of FIG. 30. In at least one embodiment, each GPC 3100 includes, without limitation, a number of hardware units for processing tasks and each GPC 3100 includes, without limitation, a pipeline manager 3102, a pre-raster operations unit (“PROP”) 3104, a raster engine 3108, a work distribution crossbar (“WDX”) 3116, a memory management unit (“MMU”) 3118, one or more Data Processing Clusters (“DPCs”) 3106, and any suitable combination of parts.

In at least one embodiment, operation of GPC 3100 is controlled by pipeline manager 3102. In at least one embodiment, pipeline manager 3102 manages configuration of one or more DPCs 3106 for processing tasks allocated to GPC 3100. In at least one embodiment, pipeline manager 3102 configures at least one of one or more DPCs 3106 to implement at least a portion of a graphics rendering pipeline. In at least one embodiment, DPC 3106 is configured to execute a vertex shader program on a programmable streaming multi-processor (“SM”) 3114. In at least one embodiment, pipeline manager 3102 is configured to route packets received from a work distribution unit to appropriate logical units within GPC 3100, in at least one embodiment, and some packets may be routed to fixed function hardware units in PROP 3104 and/or raster engine 3108 while other packets may be routed to DPCs 3106 for processing by a primitive engine 3112 or SM 3114. In at least one embodiment, pipeline manager 3102 configures at least one of DPCs 3106 to implement a neural network model and/or a computing pipeline.

In at least one embodiment, PROP unit 3104 is configured, in at least one embodiment, to route data generated by raster engine 3108 and DPCs 3106 to a Raster Operations (“ROP”) unit in partition unit 3022, described in more detail above in conjunction with FIG. 30. In at least one embodiment, PROP unit 3104 is configured to perform optimizations for color blending, organize pixel data, perform address translations, and more. In at least one embodiment, raster engine 3108 includes, without limitation, a number of fixed function hardware units configured to perform various raster operations, in at least one embodiment, and raster engine 3108 includes, without limitation, a setup engine, a coarse raster engine, a culling engine, a clipping engine, a fine raster engine, a tile coalescing engine, and any suitable combination thereof. In at least one embodiment, setup engine receives transformed vertices and generates plane equations associated with geometric primitive defined by vertices; plane equations are transmitted to coarse raster engine to generate coverage information (e.g., an x, y coverage mask for a tile) for primitive; output of coarse raster engine is transmitted to culling engine where fragments associated with primitive that fail a z-test are culled, and transmitted to a clipping engine where fragments lying outside a viewing frustum are clipped. In at least one embodiment, fragments that survive clipping and culling are passed to fine raster engine to generate attributes for pixel fragments based on plane equations generated by setup engine. In at least one embodiment, output of raster engine 3108 comprises fragments to be processed by any suitable entity such as by a fragment shader implemented within DPC 3106.

In at least one embodiment, each DPC 3106 included in GPC 3100 comprise, without limitation, an M-Pipe Controller (“MPC”) 3110; primitive engine 3112; one or more SMs 3114; and any suitable combination thereof In at least one embodiment, MPC 3110 controls operation of DPC 3106, routing packets received from pipeline manager 3102 to appropriate units in DPC 3106. In at least one embodiment, packets associated with a vertex are routed to primitive engine 3112, which is configured to fetch vertex attributes associated with vertex from memory; in contrast, packets associated with a shader program may be transmitted to SM 3114.

In at least one embodiment, SM 3114 comprises, without limitation, a programmable streaming processor that is configured to process tasks represented by a number of threads. In at least one embodiment, SM 3114 is multi-threaded and configured to execute a plurality of threads (e.g., 32 threads) from a particular group of threads concurrently and implements a Single-Instruction, Multiple-Data (“SIMD”) architecture where each thread in a group of threads (e.g., a warp) is configured to process a different set of data based on same set of instructions. In at least one embodiment, all threads in group of threads execute same instructions. In at least one embodiment, SM 3114 implements a Single-Instruction, Multiple Thread (“SIMT”) architecture wherein each thread in a group of threads is configured to process a different set of data based on same set of instructions, but where individual threads in group of threads are allowed to diverge during execution. In at least one embodiment, a program counter, call stack, and execution state is maintained for each warp, enabling concurrency between warps and serial execution within warps when threads within warp diverge. In another embodiment, a program counter, call stack, and execution state is maintained for each individual thread, enabling equal concurrency between all threads, within and between warps. In at least one embodiment, execution state is maintained for each individual thread and threads executing same instructions may be converged and executed in parallel for better efficiency. At least one embodiment of SM 3114 are described in more detail below.

In at least one embodiment, MMU 3118 provides an interface between GPC 3100 and memory partition unit (e.g., partition unit 3022 of FIG. 30) and MMU 3118 provides translation of virtual addresses into physical addresses, memory protection, and arbitration of memory requests. In at least one embodiment, MMU 3118 provides one or more translation lookaside buffers (“TLBs”) for performing translation of virtual addresses into physical addresses in memory.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, deep learning application processor is used to train a machine learning model, such as a neural network, to predict or infer information provided to GPC 3100. In at least one embodiment, GPC 3100 is used to infer or predict information based on a trained machine learning model (e.g., neural network) that has been trained by another processor or system or by GPC 3100. In at least one embodiment, GPC 3100 may be used to perform one or more neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

FIG. 32 illustrates a memory partition unit 3200 of a parallel processing unit (“PPU”), in accordance with at least one embodiment. In at least one embodiment, memory partition unit 3200 includes, without limitation, a Raster Operations (“ROP”) unit 3202; a level two (“L2”) cache 3204; a memory interface 3206; and any suitable combination thereof In at least one embodiment, memory interface 3206 is coupled to memory. In at least one embodiment, memory interface 3206 may implement 32, 64, 128, 1024-bit data buses, or similar implementations, for high-speed data transfer. In at least one embodiment, PPU incorporates U memory interfaces 3206, one memory interface 3206 per pair of partition units 3200, where each pair of partition units 3200 is connected to a corresponding memory device. For example, in at least one embodiment, PPU may be connected to up to Y memory devices, such as high bandwidth memory stacks or graphics double-data-rate, version 5, synchronous dynamic random a32ess memory (“GDDR5 SDRAM”).

In at least one embodiment, memory interface 3206 implements a high bandwidth memory second generation (“HBM2”) memory interface and Y equals half U. In at least one embodiment, HBM2 memory stacks are located on same physical package as PPU, providing substantial power and area savings compared with conventional GDDR5 SDRAM systems. In at least one embodiment, each HBM2stack includes, without limitation, four memory dies and Y equals 4, with each HBM2stack including two 128-bit channels per die for a total of 8 channels and a data bus width of 1024 bits. In at least one embodiment, memory supports Single-Error Correcting Double-Error Detecting (“SECDED”) Error Correction Code (“ECC”) to protect data. In at least one embodiment, ECC provides higher reliability for compute applications that are sensitive to data corruption.

In at least one embodiment, PPU implements a multi-level memory hierarchy. In at least one embodiment, memory partition unit 3200 supports a unified memory to provide a single unified virtual address space for central processing unit (“CPU”) and PPU memory, enabling data sharing between virtual memory systems. In at least one embodiment, frequency of accesses by a PPU to memory located on other processors is traced to ensure that memory pages are moved to physical memory of PPU that is accessing pages more frequently. In at least one embodiment, high-speed GPU interconnect 3008 supports address translation services allowing PPU to directly access a CPU's page tables and providing full access to CPU memory by PPU.

In at least one embodiment, copy engines transfer data between multiple PPUs or between PPUs and CPUs. In at least one embodiment, copy engines can generate page faults for addresses that are not mapped into page tables and memory partition unit 3200 then services page faults, mapping addresses into page table, after which copy engine performs transfer. In at least one embodiment, memory is pinned(i.e., non-pageable) for multiple copy engine operations between multiple processors, substantially reducing available memory. In at least one embodiment, with hardware page faulting, addresses can be passed to copy engines without regard as to whether memory pages are resident, and copy process is transparent.

Data from memory 3004 of FIG. 30 or other system memory is fetched by memory partition unit 3200 and stored in L2 cache 3204, which is located on-chip and is shared between various GPCs, in accordance with at least one embodiment. Each memory partition unit 3200, in at least one embodiment, includes, without limitation, at least a portion of L2 cache associated with a corresponding memory device. In at least one embodiment, lower level caches are implemented in various units within GPCs. In at least one embodiment, each of SMs 3114 may implement a level one (“L1”) cache wherein L1 cache is private memory that is dedicated to a particular SM 3114 and data from L2 cache 3204 is fetched and stored in each of L1 caches for processing in functional units of SMs 3114. In at least one embodiment, L2 cache 3204 is coupled to memory interface 3206 and XBar 3020.

ROP unit 3202 performs graphics raster operations related to pixel color, such as color compression, pixel blending, and more, in at least one embodiment. ROP unit 3202, in at least one embodiment, implements depth testing in conjunction with raster engine 3108, receiving a depth for a sample location associated with a pixel fragment from culling engine of raster engine 3108. In at least one embodiment, depth is tested against a corresponding depth in a depth buffer for a sample location associated with fragment. In at least one embodiment, if fragment passes depth test for sample location, then ROP unit 3202 updates depth buffer and transmits a result of depth test to raster engine 3108. It will be appreciated that number of partition units 3200 may be different than number of GPCs and, therefore, each ROP unit 3202 can, in at least one embodiment, be coupled to each of GPCs. In at least one embodiment, ROP unit 3202 tracks packets received from different GPCs and determines which that a result generated by ROP unit 3202 is routed to through XBar 3020.

FIG. 33 illustrates a streaming multi-processor (“SM”) 3300, according to at least one embodiment. In at least one embodiment, SM 3300 is SM 3114 of FIG. 31. In at least one embodiment, SM 3300 includes, without limitation, an instruction cache 3302; one or more scheduler units 3304; a register file 3308; one or more processing cores (“cores”) 3310; one or more special function units (“SFUs”) 3312; one or more load/store units (“LSUs”) 3314; an interconnect network 3316; a shared memory/level one (“L1”) cache 3318; and any suitable combination thereof. In at least one embodiment, a work distribution unit dispatches tasks for execution on general processing clusters (“GPCs”) of parallel processing units (“PPUs”) and each task is allocated to a particular Data Processing Cluster (“DPC”) within a GPC and, if task is associated with a shader program, task is allocated to one of SMs 3300. In at least one embodiment, scheduler unit 3304 receives tasks from work distribution unit and manages instruction scheduling for one or more thread blocks assigned to SM 3300. In at least one embodiment, scheduler unit 3304 schedules thread blocks for execution as warps of parallel threads, wherein each thread block is allocated at least one warp. In at least one embodiment, each warp executes threads. In at least one embodiment, scheduler unit 3304 manages a plurality of different thread blocks, allocating warps to different thread blocks and then dispatching instructions from plurality of different cooperative groups to various functional units (e.g., processing cores 3310, SFUs 3312, and LSUs 3314) during each clock cycle.

In at least one embodiment, Cooperative Groups may refer to a programming model for organizing groups of communicating threads that allows developers to express granularity at which threads are communicating, enabling expression of richer, more efficient parallel decompositions. In at least one embodiment, cooperative launch APIs support synchronization amongst thread blocks for execution of parallel algorithms. In at least one embodiment, applications of conventional programming models provide a single, simple construct for synchronizing cooperating threads: a barrier across all threads of a thread block (e.g., syncthreads( )function). However, In at least one embodiment, programmers may define groups of threads at smaller than thread block granularities and synchronize within defined groups to enable greater performance, design flexibility, and software reuse in form of collective group-wide function interfaces. In at least one embodiment, Cooperative Groups enables programmers to define groups of threads explicitly at sub-block(i.e., as small as a single thread) and multi-block granularities, and to perform collective operations such as synchronization on threads in a cooperative group. In at least one embodiment, programming model supports clean composition across software boundaries, so that libraries and utility functions can synchronize safely within their local context without having to make assumptions about convergence. In at least one embodiment, Cooperative Groups primitives enable new patterns of cooperative parallelism, including, without limitation, producer-consumer parallelism, opportunistic parallelism, and global synchronization across an entire grid of thread blocks.

In at least one embodiment, a dispatch unit 3306 is configured to transmit instructions to one or more of functional units and scheduler unit 3304 includes, without limitation, two dispatch units 3306 that enable two different instructions from same warp to be dispatched during each clock cycle. In at least one embodiment, each scheduler unit 3304 includes a single dispatch unit 3306 or additional dispatch units 3306.

In at least one embodiment, each SM 3300, in at least one embodiment, includes, without limitation, register file 3308 that provides a set of registers for functional units of SM 3300. In at least one embodiment, register file 3308 is divided between each of functional units such that each functional unit is allocated a dedicated portion of register file 3308. In at least one embodiment, register file 3308 is divided between different warps being executed by SM 3300 and register file 3308 provides temporary storage for operands connected to data paths of functional units. In at least one embodiment, each SM 3300 comprises, without limitation, a plurality of L processing cores 3310. In at least one embodiment, SM 3300 includes, without limitation, a large number (e.g., 128 or more) of distinct processing cores 3310. In at least one embodiment, each processing core 3310, in at least one embodiment, includes, without limitation, a fully-pipelined, single-precision, double-precision, and/or mixed precision processing unit that includes, without limitation, a floating point arithmetic logic unit and an integer arithmetic logic unit. In at least one embodiment, floating point arithmetic logic units implement IEEE 754-2008 standard for floating point arithmetic. In at least one embodiment, processing cores 3310 include, without limitation, 64 single-precision (32-bit) floating point cores, 64 integer cores, 32 double-precision (64-bit) floating point cores, and 8 tensor cores.

Tensor cores are configured to perform matrix operations in accordance with at least one embodiment. In at least one embodiment, one or more tensor cores are included in processing cores 3310. In at least one embodiment, tensor cores are configured to perform deep learning matrix arithmetic, such as convolution operations for neural network training and inferencing. In at least one embodiment, each tensor core operates on a 4×4 matrix and performs a matrix multiply and accumulate operation D=A X B+C, where A, B,C, and D are 4×4 matrices.

In at least one embodiment, matrix multiply inputs A and B are 16-bit floating point matrices and accumulation matrices C and D are 16-bit floating point or 32-bit floating point matrices. In at least one embodiment, tensor cores operate on 16-bit floating point input data with 32-bit floating point accumulation. In at least one embodiment, 16-bit floating point multiply uses 64 operations and results in a full precision product that is then accumulated using 32-bit floating point addition with other intermediate products for a 4×4×4 matrix multiply. Tensor cores are used to perform much larger two-dimensional or higher dimensional matrix operations, built up from these smaller elements, in at least one embodiment. In at least one embodiment, an API, such as CUDA 9 C++ API, exposes specialized matrix load, matrix multiply and accumulate, and matrix store operations to efficiently use tensor cores from a CUDA-C++ program. In at least one embodiment, at CUDA level, warp-level interface assumes 16×16 size matrices spanning all 32 threads of warp.

In at least one embodiment, each SM 3300 comprises, without limitation, M SFUs 3312 that perform special functions (e.g., attribute evaluation, reciprocal square root, etc.). In at least one embodiment, SFUs 3312 include, without limitation, a tree traversal unit configured to traverse a hierarchical tree data structure. In at least one embodiment, SFUs 3312 include, without limitation, a texture unit configured to perform texture map filtering operations. In at least one embodiment, texture units are configured to load texture maps (e.g., a 2D array of texels) from memory and sample texture maps to produce sampled texture values for use in shader programs executed by SM 3300. In at least one embodiment, texture maps are stored in shared memory/L1 cache 3318. In at least one embodiment, texture units implement texture operations such as filtering operations using mip-maps (e.g., texture maps of varying levels of detail), in accordance with at least one embodiment. In at least one embodiment, each SM 3300 includes, without limitation, two texture units.

Each SM 3300 comprises, without limitation, N LSUs 3314 that implement load and store operations between shared memory/L1 cache 3318 and register file 3308, in at least one embodiment. Each SM 3300 includes, without limitation, interconnect network 3316 that connects each of functional units to register file 3308 and LSU 3314 to register file 3308 and shared memory/L1 cache 3318 in at least one embodiment. In at least one embodiment, interconnect network 3316 is a crossbar that can be configured to connect any of functional units to any of registers in register file 3308 and connect LSUs 3314 to register file 3308 and memory locations in shared memory/L1 cache 3318.

In at least one embodiment, shared memory/L1 cache 3318 is an array of on-chip memory that allows for data storage and communication between SM 3300 and primitive engine and between threads in SM 3300, in at least one embodiment. In at least one embodiment, shared memory/L1 cache 3318 comprises, without limitation, 128 KB of storage capacity and is in path from SM 3300 to partition unit. In at least one embodiment, shared memory/L1 cache 3318, in at least one embodiment, is used to cache reads and writes. In at least one embodiment, one or more of shared memory/L1 cache 3318, L2 cache, and memory are backing stores.

Combining data cache and shared memory functionality into a single memory block provides improved performance for both types of memory accesses, in at least one embodiment. In at least one embodiment, capacity is used or is usable as a cache by programs that do not use shared memory, such as if shared memory is configured to use half of capacity, texture and load/store operations can use remaining capacity. Integration within shared memory/L1 cache 3318 enables shared memory/L1 cache 3318 to function as a high-throughput conduit for streaming data while simultaneously providing high-bandwidth and low-latency access to frequently reused data, in accordance with at least one embodiment. In at least one embodiment, when configured for general purpose parallel computation, a simpler configuration can be used compared with graphics processing. In at least one embodiment, fixed function graphics processing units are bypassed, creating a much simpler programming model. In general purpose parallel computation configuration, work distribution unit assigns and distributes blocks of threads directly to DPCs, in at least one embodiment. In at least one embodiment, threads in a block execute same program, using a unique thread ID in calculation to ensure each thread generates unique results, using SM 3300 to execute program and perform calculations, shared memory/L1 cache 3318 to communicate between threads, and LSU 3314 to read and write global memory through shared memory/L1 cache 3318 and memory partition unit. In at least one embodiment, when configured for general purpose parallel computation, SM 3300 writes commands that scheduler unit 3304 can use to launch new work on DPCs.

In at least one embodiment, PPU is included in or coupled to a desktop computer, a laptop computer, a tablet computer, servers, supercomputers, a smart-phone (e.g., a wireless, hand-held device), personal digital assistant (“PDA”), a digital camera, a vehicle, a head mounted display, a hand-held electronic device, and more. In at least one embodiment, PPU is embodied on a single semiconductor substrate. In at least one embodiment, PPU is included in a system-on-a-chip (“SoC”) along with one or more other devices such as additional PPUs, memory, a reduced instruction set computer (“RISC”) CPU, a memory management unit (“MMU”), a digital-to-analog converter (“DAC”), and like.

In at least one embodiment, PPU may be included on a graphics card that includes one or more memory devices. A graphics card may be configured to interface with a PCIe slot on a motherboard of a desktop computer. In at least one embodiment, PPU may be an integrated graphics processing unit (“iGPU”) included in chipset of motherboard.

Inference and/or training logic 915 are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic 915 are provided below in conjunction with FIGS. 9A and/or 9B. In at least one embodiment, deep learning application processor is used to train a machine learning model, such as a neural network, to predict or infer information provided to SM 3300. In at least one embodiment, SM 3300 is used to infer or predict information based on a trained machine learning model (e.g., neural network) that has been trained by another processor or system or by SM 3300. In at least one embodiment, SM 3300 may be used to perform one or more neural network use cases described herein.

In at least one embodiment, such components can be utilized to generate enhanced video using one or more neural networks, such as to generate a higher frame rate video from frames of a lower frame rate video.

In at least one embodiment, a single semiconductor platform may refer to a sole unitary semiconductor-based integrated circuit or chip. In at least one embodiment, multi-chip modules may be used with increased connectivity which simulate on-chip operation, and make substantial improvements over utilizing a conventional central processing unit (“CPU”) and bus implementation. In at least one embodiment, various modules may also be situated separately or in various combinations of semiconductor platforms per desires of user.

In at least one embodiment, computer programs in form of machine-readable executable code or computer control logic algorithms are stored in main memory 1304 and/or secondary storage. Computer programs, if executed by one or more processors, enable system 1300 to perform various functions in accordance with at least one embodiment. In at least one embodiment, memory 1304, storage, and/or any other storage are possible examples of computer-readable media. In at least one embodiment, secondary storage may refer to any suitable storage device or system such as a hard disk drive and/or a removable storage drive, representing a floppy disk drive, a magnetic tape drive, a compact disk drive, digital versatile disk (“DVD”) drive, recording device, universal serial bus (“USB”) flash memory, etc. In at least one embodiment, architecture and/or functionality of various previous figures are implemented in context of CPU 1302; parallel processing system 1312; an integrated circuit capable of at least a portion of capabilities of both CPU 1302; parallel processing system 1312; a chipset (e.g., a group of integrated circuits designed to work and sold as a unit for performing related functions, etc.); and any suitable combination of integrated circuit(s).

In at least one embodiment, architecture and/or functionality of various previous figures are implemented in context of a general computer system, a circuit board system, a game console system dedicated for entertainment purposes, an application-specific system, and more. In at least one embodiment, computer system 1300 may take form of a desktop computer, a laptop computer, a tablet computer, servers, supercomputers, a smart-phone (e.g., a wireless, hand-held device), personal digital assistant (“PDA”), a digital camera, a vehicle, a head mounted display, a hand-held electronic device, a mobile phone device, a television, workstation, game consoles, embedded system, and/or any other type of logic.

In at least one embodiment, parallel processing system 1312 includes, without limitation, a plurality of parallel processing units (“PPUs”) 1314 and associated memories 1316. In at least one embodiment, PPUs 1314 are connected to a host processor or other peripheral devices via an interconnect 1318 and a switch 1320 or multiplexer. In at least one embodiment, parallel processing system 1312 distributes computational tasks across PPUs 1314 which can be parallelizable—for example, as part of distribution of computational tasks across multiple graphics processing unit (“GPU”) thread blocks. In at least one embodiment, memory is shared and accessible (e.g., for read and/or write access) across some or all of PPUs 1314, although such shared memory may incur performance penalties relative to use of local memory and registers resident to a PPU 1314. In at least one embodiment, operation of PPUs 1314 is synchronized through use of a command such_as syncthreads( ) wherein all threads in a block (e.g., executed across multiple PPUs 1314) to reach a certain point of execution of code before proceeding.

Other variations are within spirit of present disclosure. Thus, while disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in drawings and have been described above in detail. It should be understood, however, that there is no intention to limit disclosure to specific form or forms disclosed, but on contrary, intention is to cover all modifications, alternative constructions, and equivalents falling within spirit and scope of disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in context of describing disclosed embodiments (especially in context of following claims) are to be construed to cover both singular and plural, unless otherwise indicated herein or clearly contradicted by context, and not as a definition of a term. Terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (meaning “including, but not limited to,”) unless otherwise noted. Term “connected,” when unmodified and referring to physical connections, is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within range, unless otherwise indicated herein and each separate value is incorporated into specification as if it were individually recited herein. Use of term “set”(e.g., “a set of items”) or “subset,” unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members. Further, unless otherwise noted or contradicted by context, term “subset” of a corresponding set does not necessarily denote a proper subset of corresponding set, but subset and corresponding set may be equal.

Conjunctive language, such as phrases of form “at least one of A, B, and C,” or “at least one of A,B and C,” unless specifically stated otherwise or otherwise clearly contradicted by context, is otherwise understood with context as used in general to present that an item, term, etc., may be either A or B or C, or any nonempty subset of set of A and B and C. For instance, in illustrative example of a set having three members, conjunctive phrases “at least one of A, B, and C” and “at least one of A, B and C” refer to any of following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of A, at least one of B, and at least one of C each to be present. In addition, unless otherwise noted or contradicted by context, term “plurality” indicates a state of being plural (e.g., “a plurality of items” indicates multiple items). A plurality is at least two items, but can be more when so indicated either explicitly or by context. Further, unless stated otherwise or otherwise clear from context, phrase “based on” means “based at least in part on” and not “based solely on.”

Operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. In at least one embodiment, a process such as those processes described herein (or variations and/or combinations thereof) is performed under control of one or more computer systems configured with executable instructions and is implemented as code (e.g., executable instructions, one or more computer programs or one or more applications) executing collectively on one or more processors, by hardware or combinations thereof In at least one embodiment, code is stored on a computer-readable storage medium, for example, in form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, a computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transitory signals (e.g., a propagating transient electric or electromagnetic transmission) but includes non-transitory data storage circuitry (e.g., buffers, cache, and queues) within transceivers of transitory signals. In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed(i.e., as a result of being executed) by one or more processors of a computer system, cause computer system to perform operations described herein. A set of non-transitory computer-readable storage media, in at least one embodiment, comprises multiple non-transitory computer-readable storage media and one or more of individual non-transitory storage media of multiple non-transitory computer-readable storage media lack all of code while multiple non-transitory computer-readable storage media collectively store all of code. In at least one embodiment, executable instructions are executed such that different instructions are executed by different processors—for example, a non-transitory computer-readable storage medium store instructions and a main central processing unit (“CPU”) executes some of instructions while a graphics processing unit (“GPU”) executes other instructions. In at least one embodiment, different components of a computer system have separate processors and different processors execute different subsets of instructions.

Accordingly, in at least one embodiment, computer systems are configured to implement one or more services that singly or collectively perform operations of processes described herein and such computer systems are configured with applicable hardware and/or software that enable performance of operations. Further, a computer system that implements at least one embodiment of present disclosure is a single device and, in another embodiment, is a distributed computer system comprising multiple devices that operate differently such that distributed computer system performs operations described herein and such that a single device does not perform all operations.

Use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments of disclosure and does not pose a limitation on scope of disclosure unless otherwise claimed. No language in specification should be construed as indicating any non-claimed element as essential to practice of disclosure.

All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

In description and claims, terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms may be not intended as synonyms for each other. Rather, in particular examples, “connected” or “coupled” may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. “Coupled” may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.

Unless specifically stated otherwise, it may be appreciated that throughout specification terms such as “processing,” “computing,” “calculating,” “determining,” or like, refer to action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within computing system's registers and/or memories into other data similarly represented as physical quantities within computing system's memories, registers or other such information storage, transmission or display devices.

In a similar manner, term “processor” may refer to any device or portion of a device that processes electronic data from registers and/or memory and transform that electronic data into other electronic data that may be stored in registers and/or memory. As non-limiting examples, “processor” may be a CPU or a GPU. A “computing platform” may comprise one or more processors. As used herein, “software” processes may include, for example, software and/or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Also, each process may refer to multiple processes, for carrying out instructions in sequence or in parallel, continuously or intermittently. Terms “system” and “method” are used herein interchangeably insofar as system may embody one or more methods and methods may be considered a system.

In present document, references may be made to obtaining, acquiring, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. Obtaining, acquiring, receiving, or inputting analog and digital data can be accomplished in a variety of ways such as by receiving data as a parameter of a function call or a call to an application programming interface. In some implementations, process of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a serial or parallel interface. In another implementation, process of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a computer network from providing entity to acquiring entity. References may also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, process of providing, outputting, transmitting, sending, or presenting analog or digital data can be accomplished by transferring data as an input or output parameter of a function call, a parameter of an application programming interface or interprocess communication mechanism.

Although discussion above sets forth example implementations of described techniques, other architectures may be used to implement described functionality, and are intended to be within scope of this disclosure. Furthermore, although specific distributions of responsibilities are defined above for purposes of discussion, various functions and responsibilities might be distributed and divided in different ways, depending on circumstances.

Furthermore, although subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that subject matter claimed in appended claims is not necessarily limited to specific features or acts described. Rather, specific features and acts are disclosed as exemplary forms of implementing the claims. 

What is claimed is:
 1. A processor comprising: one or more arithmetic logic units (ALUs) to be configured to infer frames for a video using one or more neural networks trained using one or more temporal pose representations.
 2. The processor of claim 1, wherein video frames used to train the one or more neural networks are used to generate the one or more temporal pose representations and a time-invariant appearance representation.
 3. The processor of claim 2, wherein color jittering and thin-plate-spline (TPS) warping enforce appearance invariance and localization properties for the one or more temporal pose representations.
 4. The processor of claim 2, wherein the one or more temporal pose representations comprise features represented by Gaussian heat maps and parameterized as a mean and a covariance.
 5. The processor of claim 2, wherein the one or more temporal pose representations and the time-invariant appearance representation are used to reconstruct an input video frame, and wherein a loss value resulting from comparing the input video frame with the reconstructed video frame is minimized by adjusting one or more network parameters of the one or more neural networks.
 6. The processor of claim 1, wherein motion of the one or more temporal pose representations is modeled over time using a temporal encoder including one or more long short-term memory (LSTM) networks.
 7. The processor of claim 1, wherein the inferred frames are used to generate a video with a higher frame rate, fewer dropped frames, or additional content.
 8. A system comprising: one or more processors to be configured to infer frames for a video using one or more neural networks trained using one or more temporal pose representations; and one or more memories to store the one or more neural networks.
 9. The system of claim 8, video frames used to train the one or more neural networks are used to generate a pose representation and a time-invariant appearance representation.
 10. The system of claim 9, wherein color jittering and thin-plate-spline (TPS) warping enforce appearance invariance and localization properties for the temporal pose representations.
 11. The system of claim 9, wherein the one or more temporal pose representations comprise features represented by Gaussian heat maps parameterized as a mean and covariance.
 12. The system of claim 9, wherein the pose representation and the time-invariant appearance representation are used to reconstruct an input video frame, and wherein a loss value resulting from comparing the input video frame with the reconstructed video frame is minimized by adjusting one or more network parameters of the one or more neural networks.
 13. The system of claim 8, wherein motion of the one or more temporal pose representations is modeled over time using a temporal encoder including one or more long short-term memory (LSTM) networks.
 14. A machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least: infer frames for a video using one or more neural networks trained using one or more temporal pose representations.
 15. The machine-readable medium of claim 14, wherein video frames used to train the one or more neural networks are used to generate a pose representation and a time-invariant appearance representation.
 16. The machine-readable medium of claim 15, wherein color jittering and thin-plate-spline (TPS) warping enforce appearance invariance and localization properties for the temporal pose representations.
 17. The machine-readable medium of claim 15, the one or more temporal pose representations comprise features represented by Gaussian heat maps parameterized as a mean and covariance.
 18. The machine-readable medium of claim 15, wherein the pose representation and the time-invariant appearance representation are used to reconstruct an input video frame, and wherein a loss value resulting from comparing the input video frame with the reconstructed video frame is minimized by adjusting one or more network parameters of the one or more neural networks.
 19. The machine-readable medium of claim 14, wherein motion of the one or more temporal pose representations is modeled over time using a temporal encoder including one or more long short-term memory (LSTM) networks.
 20. A processor comprising: one or more arithmetic logic units (ALUs) to train one or more neural networks, at least in part, to infer frames for a video using one or more neural networks trained using one or more temporal pose representations.
 21. The processor of claim 20, wherein video frames used to train the one or more neural networks are used to generate a pose representation and a time-invariant appearance representation.
 22. The processor of claim 21, wherein color jittering and thin-plate-spline (TPS) warping enforce appearance invariance and localization properties for the temporal pose representations.
 23. The processor of claim 21, wherein the one or more temporal pose representations comprise features represented by Gaussian heat maps parameterized as a mean and covariance.
 24. The processor of claim 21, wherein the pose representation and the time-invariant appearance representation are used to reconstruct an input video frame, and wherein a loss value resulting from comparing the input video frame with the reconstructed video frame is minimized by adjusting one or more network parameters of the one or more neural networks.
 25. The processor of claim 20, wherein motion of the one or more temporal pose representations is modeled over time using a temporal encoder including one or more long short-term memory (LSTM) networks.
 26. A system comprising: one or more processors to calculate parameters corresponding to one or more neural networks, at least in part, to infer frames for a video using one or more temporal pose representations; and one or more memories to store the parameters.
 27. The system of claim 26, wherein video frames used to train the one or more neural networks are used to generate a pose representation and a time-invariant appearance representation.
 28. The system of claim 27, wherein color jittering and thin-plate-spline (TPS) warping enforce appearance invariance and localization properties for the temporal pose representations.
 29. The system of claim 27, wherein the one or more temporal pose representations comprise features represented by Gaussian heat maps parameterized as a mean and covariance.
 30. The system of claim 27, wherein the pose representation and the time-invariant appearance representation are used to reconstruct an input video frame, and wherein a loss value resulting from comparing the input video frame with the reconstructed video frame is minimized by adjusting one or more network parameters of the one or more neural networks.
 31. The system of claim 26, wherein motion of the one or more temporal pose representations is modeled over time using a temporal encoder including one or more long short-term memory (LSTM) networks.
 32. A machine-readable medium having stored thereon a set of instructions, which if performed by one or more processors, cause the one or more processors to at least: cause one or more neural networks to be trained, at least in part, to infer frames for a video using one or more temporal pose representations; and one or more memories to store the parameters.
 33. The machine-readable medium of claim 32, wherein video frames used to train the one or more neural networks are used to generate a pose representation and a time-invariant appearance representation.
 34. The machine-readable medium of claim 33, wherein color jittering and thin-plate-spline (TPS) warping enforce appearance invariance and localization properties for the temporal pose representations.
 35. The machine-readable medium of claim 33, wherein the one or more temporal pose representations comprise features represented by Gaussian heat maps parameterized as a mean and covariance.
 36. The machine-readable medium of claim 33, wherein the pose representation and the time-invariant appearance representation are used to reconstruct an input video frame, and wherein a loss value resulting from comparing the input video frame with the reconstructed video frame is minimized by adjusting one or more network parameters of the one or more neural networks.
 37. The machine-readable medium of claim 32, wherein motion of the one or more temporal pose representations is modeled over time using a temporal encoder including one or more long short-term memory (LSTM) networks. 